Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
Face Recognition plays a major role in Biometrics. Feature selection is a measure issue in face
recognition. This paper proposes a survey on face recognition. There are many methods to extract face
features. In some advanced methods it can be extracted faster in a single scan through the raw image and
lie in a lower dimensional space, but still retaining facial information efficiently. The methods which are
used to extract features are robust to low-resolution images. The method is a trainable system for selecting
face features. After the feature selection procedure next procedure is matching for face recognition. The
recognition accuracy is increased by advanced methods.
This report is based on research. This whole research content are taken by books and websites. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we also add face recognition algorithm in report.
Now days, the task of face recognition is widely used application of image analysis as well as pattern recognition. In biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. For classifying facial expressions into different categories, it is necessary to extract important facial features which contribute in identifying proper and particular expressions. Recognition and classification of human facial expression by computer is an important issue to develop automatic facial expression recognition system in vision community. In this paper the facial expression recognition system is proposed.
This presentation of about Face Recognition. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we don't add any face recognition algorithm in presentation.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
Face Recognition plays a major role in Biometrics. Feature selection is a measure issue in face
recognition. This paper proposes a survey on face recognition. There are many methods to extract face
features. In some advanced methods it can be extracted faster in a single scan through the raw image and
lie in a lower dimensional space, but still retaining facial information efficiently. The methods which are
used to extract features are robust to low-resolution images. The method is a trainable system for selecting
face features. After the feature selection procedure next procedure is matching for face recognition. The
recognition accuracy is increased by advanced methods.
This report is based on research. This whole research content are taken by books and websites. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we also add face recognition algorithm in report.
Now days, the task of face recognition is widely used application of image analysis as well as pattern recognition. In biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. For classifying facial expressions into different categories, it is necessary to extract important facial features which contribute in identifying proper and particular expressions. Recognition and classification of human facial expression by computer is an important issue to develop automatic facial expression recognition system in vision community. In this paper the facial expression recognition system is proposed.
This presentation of about Face Recognition. you can learn about face recognition history, how's it is work traditional and in technical way, introduction of some face recognition software and devices. we don't add any face recognition algorithm in presentation.
AN IMPROVED TECHNIQUE FOR HUMAN FACE RECOGNITION USING IMAGE PROCESSINGijiert bestjournal
Face recognition is a computer application technique for automatically identifying or
verifying a person from a digital image or a video frame source. To do this is by comparing
selected facial features from the digital image and a face dataset. It is basically used in
security systems and can be compared to other biometrics such as fingerprint recognition or
eye, iris recognition systems. The main limitation of the current face recognition system is
that they only detect straight faces looking at the camera. Separate versions of the system
could be trained for each head orientation, and the results can be combined using arbitration
methods similar to those presented here. In earlier work, the face position must be centerlight
position; any lighting effect will affect the system. Similarly the eyes of person must be
open and without glass.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
Face Liveness Detection for Biometric Antispoofing Applications using Color T...rahulmonikasharma
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
Assessment and Improvement of Image Quality using Biometric Techniques for Fa...ijceronline
Biometrics is broadly used in Forensic, highly secured control access and prison security. By making use of this system one can recognizes a person by determining the authentication by his or her biological and physiological features such as Fingerprint, retina-scan, iris scans and face recognition. The determination of the characteristic function of quality and match scores shows that a careful selection of complimentary sets of quality metrics can provide much more benefit to various benefits of biometric quality. Face recognition is a challenging approach to the image quality analysis and many more security applications. Biometric face recognition is the well known technology which is used by the government and civilian applications such Aadhar cards, Pan cards etc. Face recognition is a Behavioral and physiological feature of a human being. Nowadays the quality of an biometric image is the measure concern. There are many factors which are directly or indirectly affects on the image quality hence improvement in image quality has to be done by making the use of some biometric techniques for face recongnion.This paper presents some important techniques for fake biometric detection and improvement of facial image quality.
NEC NEOFACE- Biometric Face Recognition SystemNECIndia
NEC NeoFace combines an extracted analysis of the eyes with a
detailed determination of facial feature, using a GLVQ based multiple matching face recognition system.Providing a reliable verification solution.
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
The goal of this paper is to present a critical survey of existing literatures on human face detection and recognition over the last 4-5 years. An application for automatic face detection and tracking in video streams from surveillance cameras in public or commercial places is discussed in this paper. Prototype is designed to work with web cameras for the face detection and tracking system based on Visual 2010 C# and Open CV. This system can be used for security purpose to record the visitor face as well as to detect and track the face.
Keywords:- Face Detection, Face Recognition, Open CV, Face Tracking, Video Streams.
Assessment of water supply facilities in Owo Local Government Area, Ondo Stat...IOSR Journals
The current study was carryout to investigate the status of water supply facilities in 24 rural
communities of Owo local government area of Ondo State, Nigeria. Former and informer interview,
questionnaire and physical assessment conducted. Secondary data from Nigeria National Population
Commission (NNPC) were used in this study. The major water supply facilities used by the communities were
mostly hand dug well and boreholes which in most cases were fitted with either electric or hand pump. It was
observed that all the boreholes fitted with hand pump were failed while 86 % of those fitted with electrics pump
were also failed. In the case of the hand dug well more than 37 % of all the hand dug well were failed. Borehole
failure was due to people ignorance, non availability of spare parts, constant water failure, poor maintenance
skills and attitude of the communities. The failures of the hand dug well were mainly due to low water table or
aquifer region. The survey assessment results revealed that sustainable water supply to the community could be
enhancing through the use of hand pump boreholes. Hand pump boreholes appeared more reliable with low
operational technology, their cost effectiveness affordability and available spare parts. The studies recommend
the involvement of the community participation in the overall management of the water facility in other to
enhance sustainability.
A study of techniques for facial detection and expression classificationIJCSES Journal
Automatic recognition of facial expressions is an important component for human-machine interfaces. It
has lot of attraction in research area since 1990's.Although humans recognize face without effort or
delay, recognition by a machine is still a challenge. Some of its challenges are highly dynamic in their
orientation, lightening, scale, facial expression and occlusion. Applications are in the fields like user
authentication, person identification, video surveillance, information security, data privacy etc. The
various approaches for facial recognition are categorized into two namely holistic based facial
recognition and feature based facial recognition. Holistic based treat the image data as one entity without
isolating different region in the face where as feature based methods identify certain points on the face
such as eyes, nose and mouth etc. In this paper, facial expression recognition is analyzed with various
methods of facial detection,facial feature extraction and classification.
Face Liveness Detection for Biometric Antispoofing Applications using Color T...rahulmonikasharma
Face recognition is a widely used biometric approach. Face recognition technology has developed rapidly in recent years and it is more direct, user friendly and convenient compared to other methods. But face recognition systems are vulnerable to spoof attacks made by non-real faces. It is an easy way to spoof face recognition systems by facial pictures such as portrait photographs. A secure system needs Liveness detection in order to guard against such spoofing. In this work, face liveness detection approaches are categorized based on the various types techniques used for liveness detection. This categorization helps understanding different spoof attacks scenarios and their relation to the developed solutions. A review of the latest works regarding face liveness detection works is presented. The main aim is to provide a simple path for the future development of novel and more secured face liveness detection approach.
Assessment and Improvement of Image Quality using Biometric Techniques for Fa...ijceronline
Biometrics is broadly used in Forensic, highly secured control access and prison security. By making use of this system one can recognizes a person by determining the authentication by his or her biological and physiological features such as Fingerprint, retina-scan, iris scans and face recognition. The determination of the characteristic function of quality and match scores shows that a careful selection of complimentary sets of quality metrics can provide much more benefit to various benefits of biometric quality. Face recognition is a challenging approach to the image quality analysis and many more security applications. Biometric face recognition is the well known technology which is used by the government and civilian applications such Aadhar cards, Pan cards etc. Face recognition is a Behavioral and physiological feature of a human being. Nowadays the quality of an biometric image is the measure concern. There are many factors which are directly or indirectly affects on the image quality hence improvement in image quality has to be done by making the use of some biometric techniques for face recongnion.This paper presents some important techniques for fake biometric detection and improvement of facial image quality.
NEC NEOFACE- Biometric Face Recognition SystemNECIndia
NEC NeoFace combines an extracted analysis of the eyes with a
detailed determination of facial feature, using a GLVQ based multiple matching face recognition system.Providing a reliable verification solution.
A facial recognition system is a technology capable of identifying or verifying a person from a digital image or a video frame from a video source.
This slide is all about a detailed description of the Face Recognition System.
The goal of this paper is to present a critical survey of existing literatures on human face detection and recognition over the last 4-5 years. An application for automatic face detection and tracking in video streams from surveillance cameras in public or commercial places is discussed in this paper. Prototype is designed to work with web cameras for the face detection and tracking system based on Visual 2010 C# and Open CV. This system can be used for security purpose to record the visitor face as well as to detect and track the face.
Keywords:- Face Detection, Face Recognition, Open CV, Face Tracking, Video Streams.
Assessment of water supply facilities in Owo Local Government Area, Ondo Stat...IOSR Journals
The current study was carryout to investigate the status of water supply facilities in 24 rural
communities of Owo local government area of Ondo State, Nigeria. Former and informer interview,
questionnaire and physical assessment conducted. Secondary data from Nigeria National Population
Commission (NNPC) were used in this study. The major water supply facilities used by the communities were
mostly hand dug well and boreholes which in most cases were fitted with either electric or hand pump. It was
observed that all the boreholes fitted with hand pump were failed while 86 % of those fitted with electrics pump
were also failed. In the case of the hand dug well more than 37 % of all the hand dug well were failed. Borehole
failure was due to people ignorance, non availability of spare parts, constant water failure, poor maintenance
skills and attitude of the communities. The failures of the hand dug well were mainly due to low water table or
aquifer region. The survey assessment results revealed that sustainable water supply to the community could be
enhancing through the use of hand pump boreholes. Hand pump boreholes appeared more reliable with low
operational technology, their cost effectiveness affordability and available spare parts. The studies recommend
the involvement of the community participation in the overall management of the water facility in other to
enhance sustainability.
Speciation And Physicochemical Studies of Some Biospecific CompoundsIOSR Journals
Abstract: A green, safer , efficient , eco-friendly approach for the synthesis of novel compounds which reveal biological and spermicidal activity. The nature of the pharmacophore decides the physiological reactivity of the compound.
Proficient Handling and Restraint of the Laboratory Animal Rat (Rattus Norveg...IOSR Journals
The laboratory rat is an important animal model which has been used extensively in the fields of biological, pharmaceutical, behavioral and biomedical sciences. There are several laboratory procedures which are implemented on this model repetitively. These procedures require proper handling and restraint of the rat. A good amount of general information is available at several places on web. Knowledge about safe and effective rat handling techniques and methods are mandatory to learn before starting experiments on animal models. Avoidance of stress and discomfort of the rat is very important for the overall outcome of an experimental study. Here we address and review someessential techniques to handle difficulties of working with the laboratory rat (RattusNorvegicus) using our first-hand experience from an animal care and safety perspective in moderately available animal facility especially for developing countries.
Herbal Cures Practised By Rural Populace In Varanasi Region Of Eastern U.P.(I...IOSR Journals
A survey based study to collect information regarding use of herbs as household treatment of common ailments in rural areas of Varanasi region of eastern U.P. was undertaken .In Varanasi as in other parts of India , the people especially those residing in rural and semi-urban areas still practise herbal cures for many of their ailments. In the present investigation a total of 40 medicinally important plant species belonging to 27 families were recorded which are frequently used by local populace to cure diseases such as cold,cough,fever,snake bite,boils piles etc.As plants are easily available and sometimes the only source of healthcare available to poor therefore there is a great need for preservation of such medicinal plants.
Application of Artificial Neural Network (Ann) In Operation of ReservoirsIOSR Journals
Abstract: Reservoir operation is an important element in water resources planning and management. It consists
of several parameters like inflow, storage, evaporation and demands that define the operation strategies for
giving a sequence of releases to meet the demands. The operating policy is a set of rules for determining the
quantities of water to be stored or released or withdrawn from a reservoir or system of several reservoirs under
various conditions. Reservoir operation frequently follows a conventional policy based on Guide curves (Rule
curves) that prescribes reservoir releases based on limited criteria such as current storage levels, season and
demands. Operating policies can be derived using system techniques such as simulation, optimisation and
combination of these two. System analysis has proved to be a potential tool in the planning, operation and
management of the available resources. In recent years, artificial intelligence techniques like Artificial Neural
Networks (ANN) have arisen as an alternative to overcome some of the limitations of traditional methods. In
most of the studies, feed forward structure and the back propagation algorithm have been used to design and
train the ANN models respectively. Detail analysis will be carried out to develop an ANN model for reservoir
operation and assess the application potential of ANN in attaining the reservoir operation objectives compared
with the conventional rule curves.
Key words:Artificial Neural Network, Back propagation, Guide curves (Rule curves), optimisation,reservoir
operation, simulation.
Structural and Spectroscopical Studies for a Complex Macromolecule (hGH)IOSR Journals
Abstract: This paper tries to find the effect of the different environmental changes of ( pH, ionic strength, and temperature) on their tertiary structure of hGH by using UV sepctoscopy. We found that the hormone affected by chemical reagents change its conformation and folding. The present study is an attempt to discover what could happen to GH molecule when the biochemistry of body is changed. The results reveal that tryptophyl residues are inside the hormone, whereas tyrosyl residues are on the surface.
A Digital Pen with a Trajectory Recognition AlgorithmIOSR Journals
Abstract : Now a days, the development of miniaturization technologies in electronic circuits and components has seriously decreased the dimension and weight of consumer electronic products, those are smart phones and handheld computers, and thus prepared them more handy and convenient. This paper contains an accelerometer-based digital pen for handwritten digit and gesture trajectory recognition applications. The digital pen consists of a triaxial accelerometer, a microcontroller, and an Zigbee wireless transmission module for sensing and collecting accelerations of handwriting and gesture trajectories. with this project we can do human computer interaction. Users can utilize this pen to write digits or make hand gestures, and the accelerations of hand motions calculated by the accelerometer are wirelessly transmitted to a computer for online trajectory recognition. So, by varying the position of mems (micro electro mechanical systems) we can capable to show the alphabetical characters in the PC. The acceleration signals calculated from the triaxial accelerometer are transmitted to a computer via the wireless module. Keywords - ARM, Zigbee, Sensors module
A Fossil Dicot Wood Aeschynomenoxylon Mohgaonsesp.Nov From The Deccan Intertr...IOSR Journals
The Deccan Intertrappean flora is mostly silicified and often very well preserved, representing the groups Thallophyta (fungi, algae and charophytes), water ferns, conifers and angiosperms with both monocotyledons and dicotyledons. The present wood is collected from the Deccan Intertrappean beds of Mohgaonkalan. The wood is angiospermic diffuse porus, vessels solitary and in multiples of two, usually small, occasionally of medium size. Xylem parenchyma scanty with paratracheal vasicentri type, Intervascular pitting are simple and alternate.Perforation plate simple. Fibers are non-septate and storied. Wood rays uniseriate only. Rays are homogenous and made up of procumbent cells only. It show its affinities with the reported species of Aeschynomene.
To find a non-split strong dominating set of an interval graph using an algor...IOSR Journals
In graph theory, a connected component of an undirected graph is a sub graph in which any two
vertices are connected to each other by paths. For a graph G, if the subgraph of G itself is a connected
component then the graph is called connected, else the graph G is called disconnected and each connected
component sub graph is called it’s components. A dominating set Dst of graph G=(V,E) is a non-split strong
dominating set if the induced sub graph < V-Dst > is connected. The non-split strong domination number of G is
the minimum cardinality of a non-split strong dominating set . In this paper constructed a verification method
algorithm for finding a non-split strong dominating set of an interval graph.
Complexation, Spectroscopic, Thermal, Magnetic And Conductimetric Studies On ...IOSR Journals
7-hydroxy-4-methyl-8-(phenylazo) coumarin (L1)and 7-hydroxy-4-methyl-8-(o-carboxyphenylazo) coumarin (L2) have been prepared and characterized by elemental analysis, infrared (IR), proton nuclear magnetic resonance (1H NMR) and Mass spectra. The important infrared (IR) spectral bands corresponding to the active groups in the two ligands and the solid complexes under investigation were studied. Also the important fragments in the ligands and the complexes were done using mass spectra and the main peaks were corresponding to the molecular weights of the ligands and complexes. The solid complexes have been synthesized and studied by elemental and thermal analyses (TG and DTA) as well as by IR, 1H NMR, magnetic measurements, electronic transition, molar conductance, mass spectra and electron spin resonance (ESR) spectra. The proposed steriochemical structures for the investigated metal complexes suggest octahedral geometry with respect to Mn, Co, Ni, Cu and Zn metal ions and all of the formed complexes contain coordinated and hydrated water molecules. All of the prepared solid complexes behave as non-electrolytes in chloroform.
Survey on Single image Super Resolution TechniquesIOSR Journals
Super-resolution is the process of recovering a high-resolution image from multiple lowresolutionimages
of the same scene. The key objective of super-resolution (SR) imaging is to reconstruct a
higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘lowresolution’
images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for
facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review
of existing super-resolution techniques and highlight the future research challenges. This includes the
formulation of an observation model and coverage of the dominant algorithm – Iterative back projection.We
critique these methods and identify areas which promise performance improvements. In this paper, future
directions for super-resolution algorithms are discussed. Finally results of available methods are given.
Transformational Leadership at Muhammadiyah Primary Schoolson Emotional Intel...IOSR Journals
The aim of this research are examines the influence of Authentic Transformational Leadership behavior on Emotional Intelligence with intervening variables: Value CongruenceandTrust of the teachers and employees at MuhammadiyahPrimary Schools forward Bass &Avolio Theory. Quantitative approach used on this research. The samples in this study were 55 employees and 110 teachers. The finding indicate that Authentic Transformational Leadership behavior have significant direct effect on Emotional Intelligence. The significant influence was also shown by intervening variables: Value Congruence (positive), andTrust (negative). For further studies there is recommended to conduct similar studies in high school Muhammadiyah, considering there are differences in the curriculum and the emotional maturity of students and teachers
Promoting Industrial Training through Macro Economic Approach (The Importance...IOSR Journals
Libya is blessed with many factories but regrettably these factories failed due to lack of skills and experiences. Often Libya due to their uncoordinated, unregulated and fragmented nature delivery systems and policies are the challenges faced by the state. It is difficult to design a training system that ensures demand driven skills provision and involves stakeholders from key relevant sectors and this requires a study to identify problems and prosper solution for sustainable future development. Hence, the report adopted the approach which combines the results of studies being reviewed. I utilized analytical techniques to estimate the strength of a given set of findings across many different studies and sometime compare and draw conclusion. This has allowed the creation of a context from which this report emerged The report data solely rely on the empirical source which classified in primary and secondary source.The reported found out that the number of trainee dropped from 2000 to 2005 by an average of 26 students, in comparison to 1999. However, in 2006 and 2007, the number of trainee showed increased (by an average of 25 students). Due to this increased in trainee, oil production also increased at the beginning of the new millennium. This Indicate that training increase productivity and productivity growth can raise incomes and reduce poverty in a virtuous circle. Productivity growth reduces production costs and increases returns on investments, some of which turn into income for business owners and investors and some of which are turned into higher wages and national growth.
Synthesis and Characterization O-, M- and Para-Toluyl Thiourea Substituted Pa...IOSR Journals
Abstract: Six new derivatives of carbonyl thiourea comprises of o-,m- and p-toluyl at one end of Nitrogen atom and p-methylpyridine or ethyl pyridine at another one end of Nitrogen atom has been synthesized. The compounds are, 2-methyl-N-[(4-methylpyridine-2-yl) carbamothiol] benzamide (I), 3-methyl-N-[(4-methylpyridine-2-yl) carbamothiol] benzamide (II) and 4-N-[(4-methylpyridine-2-yl) carbamothiol] benzamide (III) for Toluyl-MP while 2-methyl-N-[(2-pyridine-2-yl-ethyl) carbamothiol] benzamide (IV), 3- methyl-N-[(2-pyridine-2-yl-ethyl) carbamothiol] benzamide (V) and 4- methyl-N-[(2-pyridine-2-yl-ethyl) carbamothiol] benzamide (VI) for isomer Toluidal-AEP have been successfully synthesized and characterized by elemental analysis, Infrared Spectroscopy analysis (FT-IR), Nuclear Magnetic Resonance Spectroscopy (NMR) and Ultraviolet-visible (UV-vis). All products shown stretching modes of ν(N-H), ν(C=O), ν(C-N), and ν(C=S) around 3276 cm-1, 1671 cm-1, 1315cm-1 and 1148 cm-1 respectively. All products shown two maximum absorption around 262 nm and 290 nm respectively for carbonyl C=O and thione C=S chromophore. Those both values contributed by n -п* transition. 1H nuclear magnetic resonance spectrum showed presence of aromatic, methyl, methine and amide protons except for product III. All products showed presence of carbon thione in 13C nuclear magnetic resonance except for product III. Ionophor interpretation with acetate anion shows color changes by naked eye for compound (I) and (III).
Lipid oxidation and perceived exertion level during exercise in obese: effect...IOSR Journals
Regular exercise is one of the most used solutions to avoid obesity. In this study we compared the amounts of lipid oxidation and the level of perceived exertion in three physical exercises, one continuous and two intermittent in obese. Ten obese men (age 26.01 ± 6.0 years, weight: 104.2 ± 19.4 kg, BMI: 33.5 ± 3.6 kg / m2) performed three 45 minutes exercises during which we measured energy expenditure and the level of perceived exertion. A continuous exercise whose intensity corresponds to the intensity of Fat max, an intermittent exercise which alternate four minutes at the intensity of Fat max -10% and one minute at the fat max intensity +10% (intermittent 1/4), and a second intermittent exercise which alternate two minutes at the intensity of Fat max -10% and one minute at the Fat max intensity +20% (intermittent 1/2). While the total energy expenditure during continuous exercise (321.6 Kcal) is higher than those of the intermittent 1/4 (268.1 Kcal) and the intermittent 1/2 (268.9 Kcal), the amounts of energy from oxidized fats in the three exercises are equivalent: 34,6 Kcal, 31,8 Kcal and 36,2 Kcal respectively for the three exercises. The perceived exertion measured by the Borg scale showed that intermittent exercises causes less fatigue in obese than the continuous exercise.
Eleven (11) composites, are unfilled and five each filled with varying contents of charcoal and wood dust separating bonded with melted spent thermoplastics have been compounded and their compressive strength, density, specific gravity, percentage shrinkage and percentage absorption determined.Composite density ranges (0.83-0.94)g/cm3, compressive strength from (6.92-16.14)n/mm2, percentage shrinkage from (1.01-4.76)% and percentage water absorption from (1.75-15.75)%. These results suggest that these composites (i) meet the allowable American standard for test and measurement (ASTM) for ceramic floor and wall tiles in compressor strength (0.2-22.1)minimum, % shrinkage (maximum 15%) and water absorption (maximum 16%) (ii)are appreciably strong compare to the gmelina-wood/cement composite and (iii) if comprehensively examined, composites may be useful in the building industry for the manufacture of tiles and boards and even compete favorably with the wood cement composites.
"Study of Java Access Control Mechanism”IOSR Journals
Abstract: a class as "a collection of data and methods." One of the important object-oriented techniques is
hiding the data within the class and making it available only through the methods. This technique is known as
encapsulation because it seals the data (and internal methods) safely inside the "capsule" of the class, where it
can be accessed only by trusted users.
Another reason for encapsulation is to protect your class against accidental or willful stupidity. A class
often contains a number of interdependent fields that must be in a consistent state. If you allow a users to
manipulate those fields directly, he may change one field without changing important related fields, thus leaving
the class in an inconsistent state. If, instead, he has to call a method to change the field, that method can be sure
to do everything necessary to keep the state consistent. Similarly, if a class defines certain methods for internal
use only, hiding these methods prevents users of the class from calling them.
The goal is : All the fields and methods of a class can always be used within the body of the class itself. Java
defines access control rules that restrict members of a class from being used outside the class that is done
through keeping the data (and internal methods) safely inside the "capsule" of the class, where it can be
accessed only by trusted users.
Keywords: Encapsulation, Access Control, Classes, Constructors, Methods.
HUMAN FACE RECOGNITION USING IMAGE PROCESSING PCA AND NEURAL NETWORKijiert bestjournal
Security and authentication of a person is a vital part of any business. There are many techniques use d for this purpose. One of technique is human face recognition . Human Face recognition is an effective means of authenticating a person. The benefit of this approa ch is that,it enables us to detect changes in the face pattern of an individual to substantial extent. The recognition s ystem can tolerate local variations in the face exp ression of an individual. Hence Human face recognition can be use d as a key factor in crime detection mainly to iden tify criminals. There are several approaches to Human fa ce recognition of which Image Processing Principal Component Analysis (PCA) and Neural Networks have been includ ed in our project. The system consists of a databas e of a set of facial patterns for each individual. The charact eristic features called �eigenfaces� are extracted from the stored images using which the system is trained for subseq uent recognition of new images.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Innovative Analytic and Holistic Combined Face Recognition and Verification M...ijbuiiir1
Automatic recognition and verification of human faces is a significant problem in the development and application of Human Computer Interaction (HCI).In addition, the demand for reliable personal identification in computerized access control has resulted in an increased interest in biometrics to replace password and identification (ID) card. Over the last couple of years, face recognition researchers have been developing new techniques fuelled by the advances in computer vision techniques, Design of computers, sensors and in fast emerging face recognition systems. In this paper, a Face Recognition and Verification System has been designed which is robust to variations of illumination, pose and facial expression but very sensitive to variations of the features of the face. This design reckons in the holistic or global as well as the analyticor geometric features of the face of the human beings. The global structure of the human face is analysed by Principal Component Analysis while the features of the local structure are computed considering the geometric features of the face such as the eyes, nose and the mouth. The extracted local features of the face are trained and later tested using Artificial Neural Network (ANN). This combined approach of the global and the local structure of the face image is proved very effective in the system we have designed as it has a correct recognition rate of over 90%.
Humans often use faces to recognize individuals, and advancements in computing capability over the past few decades now enable similar recognitions automatically. Early facial recognition algorithms used simple geometric models, but the recognition process has now matured into a science of sophisticated mathematical representations and matching processes. Major advancements and initiatives in the past 10 to 15 years have propelled facial recognition technology into the spotlight. Facial recognition can be used for both verification and identification.
Cross Pose Facial Recognition Method for Tracking any Person's Location an Ap...ijtsrd
In todays world, there are number of existing methods for facial recognition. These methods are based on frontal view face data. There are few methods which are based on non-frontal view face recognition method. In most of the face recognition algorithm, œFeature space approach is used. In this approach, different feature vectors are extracted from face. These distances are compared to determine matches. In this paper, it is proposed that how any person can be located in a campus or in a city using a cross pose face recognition method. This paper is focusing on three parts 1) generation of multi-view images 2) comparison of images 3) showing the actual location of a person. Sanjay D. Sawaitul"Cross Pose Facial Recognition Method for Tracking any Persons Location an Approach" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: http://www.ijtsrd.com/papers/ijtsrd7186.pdf http://www.ijtsrd.com/computer-science/data-processing/7186/cross-pose-facial-recognition-method-for--tracking-any-persons-location-an-approach/sanjay-d-sawaitul
Face detection is one of the most suitable applications for image processing and biometric programs. Artificial neural networks have been used in the many field like image processing, pattern recognition, sales forecasting, customer research and data validation. Face detection and recognition have become one of the most popular biometric techniques over the past few years. There is a lack of research literature that provides an overview of studies and research-related research of Artificial neural networks face detection. Therefore, this study includes a review of facial recognition studies as well systems based on various Artificial neural networks methods and algorithms.
Scale Invariant Feature Transform Based Face Recognition from a Single Sample...ijceronline
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Techniques for Face Detection & Recognition Systema Comprehensive Review
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 15, Issue 5 (Nov. - Dec. 2013), PP 01-12
www.iosrjournals.org
www.iosrjournals.org 1 | Page
Techniques for Face Detection & Recognition System-
a Comprehensive Review
Vandana S.Bhat1
, Dr. J. D. Pujari2
1
Department of Information Science & Engineering, SDMCET, Dharwad, INDIA
2
Department of Information Science & Engineering, SDMCET, Dharwad, INDIA
Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address
many contemporary prerequisites for identification and the verification of identity prerogatives. It brings
together the potential of supplementary biometric systems, which attempt to link identity to individually
distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current
decades face recognition has experienced significant consideration from both research communities and the
marketplace, conversely still remained very electrifying in real applications. The assignment of face detection
and recognition has been dynamically researched in current eternities. This paper offers a conversant
evaluation of foremost human face recognition research. We first present a summary of face detection, face
recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques
is accessible.
Clarification and restrictions of the performance of these face recognition algorithms are specified.
Here we present a vital assessment of the current researches concomitant with the face recognition process. In
this paper, we present a broad range review of major researches on face recognition process based on various
circumstances. In addition, we present a summarizing description of Face detection and recognition process
and development along with the techniques connected with the various influences that affects the face
recognition process.
Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.
I. INTRODUCTION
For obvious reasons, the visual organs and the human brain is one of the best existing face recognition
machine ever and will be forever. Fusiform face area- a specific are of brain has found to be the totally
dedicated part for this task.
As the face recognition is considered to be the most natural thing for humans, biometric techniques are
considered to be most suitable for this purpose. Human face recognition is the most authenticated and widely
used step for identity proof. A simple example of this can be seen in our wallets, driving license, membership
cards etc.
The apparent use of face recognition led to many fantasies, difficulties and inconsistencies while
implementing automatic face recognition in technical field. This is because the technology always will have
brain as the outstanding competitor trained from birth to do exact things somehow.
An exponential growth in this field has thus has involved the sophisticated features of almost all the
biometric techniques up to point where it is on the verge to compete with greatest competitor: the human brain is
the best face recognition machine ever and may be forever. Still, in order to deliver its full efficiency, a number
of caveats have to be taken into account during implementation, feature extraction is also a main component of a
face recognition system, and an example of Eigen face based face recognition system is shown in figure below:
Figure 1. Eigen faces, courtesy of Santiago Serrano Drexel University, USA.
2. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 2 | Page
Face detection has direct relevance with the face recognition because before recognition the image
must be analysed and the location of the face must be detected. The whole process of face detection consists of
four steps:
i. Input: - An image passed to the system as input .The image may vary in format, size and resolution.
ii. Pre-processing:-The image is pre-processed to remove the background noise. This is also called image
normalization.
iii. Classifier: - it takes decision whether the image belong to the face or non-face class.
iv. Output: - This indicates the location of the face in the original image input.
Figure 2. Basic building of face detection system
The block diagram of a typical face recognition system can be shown with the help of Figure 2.The
face detection and face extraction are carried out simultaneously. The complete process of face recognition can
be shown in the Figure 2. The face recognition system works in three stages: face detection, feature extraction
and face recognition. The face recognition systems are divided into three regions; holistic approaches in which
original image used as input means the face is used as the raw data. Second category is of feature based
techniques in which the local features are first extracted and the localized image is used as raw input to next
stage. The third category belongs to hybrid approaches in which both the local features and face region are used
as input.
Figure 3. Basic building of face recognition system
The first step in face recognition system is to detect the face in an image. The main objective of face
detection is to find whether there are any faces in the image or not. If the face is present, then it returns the
location of the image and extent of the each face. Pre-processing is done to remove the noise and reliance on the
precise registration.
There are a lot of factors due to which the face detection is a challenging task.
Pose
Presence or absence of structural components
Facial expression
Occlusion
Image orientation
Imaging conditions
Input
Face detection
Feature Extraction
Face recognition
Input
Pre-processing
Classifier
Output
3. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 3 | Page
Figure 4: examples of pose angles for a face in a biometric system.
A. Face As biometric
Face recognition is superior than other biometric techniques in certain circumstances, and also the
corresponding weakness that renders it unsuitable choice as a biometric technique for other applications. Face
recognition came as a biometric derives a major advantage of being the primary biometric used by humans to
recognize each other.
The main drawbacks to face recognition are its current relatively low accuracy (compared to the proven
performance of finger print and iris recognition) and the relative ease with which many systems can be defeated.
Finally, there are many attributes leading to the variability of images of a single face that add to the complexity
of the recognition problem if they cannot be avoided by careful design of the capture situation. Inadequate
constraint or handling of such variability inevitably leads to failures in recognition. These include:
Physical changes: for ex. facial expression change; aging; personal appearance (make-up, glasses,
facial hair, hairstyle, disguise).
Acquisition geometry changes: change in location, scale and in-plane rotation of the face (facing the
camera) as well as rotation in depth (facing the camera obliquely, or presentation of a profile, not full-
frontal face).
Imaging changes: camera variations; lighting variation; channel characteristics (especially in
compressed images, or broadcast).
Figure 5: Sample variations of a single face: in pose, facial appearance, age, lighting and expression.
Face recognition today has to overcome some prominent challenges like broad lighting change and handling
rotation in depth, together along with personal appearance changes. Accuracy needs to be improved even under
good conditions.
B. Robustness and Fraud
All biometric recognition systems are susceptible to accidental errors of two types which both must be
minimized: False Accept (FA)errors where a random impostor is accepted as a legitimate users and False Reject
(FR) errors where a legitimate user is denied access. Designers of biometric systems must also be very
conscious of how the system will behave when deliberately attacked..
It is also possible for some people to impersonate others with a high degree of similarity (an important
vulnerability in ‗cooperative‘ applications like physical access control). A couple of years ago, few systems had
a test to detect authenticity (rejecting objects that looked too flat to be faces rather than photographs), but a
recent PC Magazine test found that both systems tested could distinguish a real person from a photograph. More
sophisticated shape algorithms could be devised, and elastic deformation can be used to prevent simple
photograph replay attacks. (One system allows the option of requiring a change in facial expression during
verification.) With computing power more abundant, the technology for detecting fake biometrics will keep
improving.
4. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 4 | Page
Figure 6: False acceptance – false rejection graph.
C. Structure of Assessment
The association steps of this paper are as follows. The Introductory Section ends with a brief
introduction of Face detection and recognition process and its necessity in biometric system. The part
in introduction shows a brief explanation about principle of face as biometric and its robustness factor.
In Section II, explains a General review of face detection techniques, this process is difficult because although
commonalities exist between faces, they can differ considerably in terms of age, skin colour and facial
expression. An ideal face detector must be able to detect the faces under any set of conditions.
Section III provide the information about the review on recent researches in traditional feature extraction based
unsupervised approaches, this research therefore has considered a review on existing face recognition
techniques based on these approaches, this section consist of some recent researches on Eigen face based
methods, Dynamic Graph Matching Algorithms, Geometrical Feature Matching, Template Matching
Algorithms, Line Edge Map (LEM) based methods.
Section IV addresses the intelligent supervised approaches in face recognition including
neural network, Hidden Markov model, support vector machine, Fuzzy theory etc.
Section V Shows the comparison of different face database used in the field of face recognition and a general
conclusion of the paper, discussion regarding review is presented in Section VI.
II. RECENT SURVEY ON AVAILABLE TECHNIQUES FOR FACE DETECTION
Face detection is one of the easiest tasks for human brain. But for the technology it is one of the most
complicated tasks. Primary solution includes the segmentation, extraction and verification of faces and its
features from an uncontrolled background. But is expected to advance up to a level where it can smoothly
function irrespective of camera distance, illumination and orientation.
Figure 7: Typical training images for face recognition.
We analyze that Face detection divided into approaches:
Face Detection
Feature Based Approaches
o Low Level Analysis
o Feature Analysis
o Active Shape Model
Image Based Approaches
o Linear Subspace methods
o Neural network
o Statical Approaches
Some methods that fall into each category are edge based, skin color based, gray level based and motion based,
point distribution model and deformable template are the type active shape model [1].
Edge is the most primitive feature in computer vision applications and it was applied in some earlier
face detection techniques by Sakai et al. [10]. It was based on analyzing line drawings of faces to locate facial
features. Craw et al. [2] designed hierarchical method to trace human head outline based on Sakai‗s work. More
recent examples of edge based techniques can be found in [3, 4, and 5] for facial feature extraction and in [6, 7,
and 8] for face detection. Govinda raju [9] accomplishes this by labeling edges as the left side, hairline, or right
side of a front view face and matches these edges against a face model by using the golden ratio [11].
5. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 5 | Page
While gray level information gives us basic features about face representation, color information can
provide us more face features by extra dimensions of pixel representation. [12, 13, and 14]. The most common
color model is RGB representation in which colors are defined by combinations of red, green, blue color
components. [12, 14, 15, 16, 17, and 18].
When use of video sequence is available, motion information can also be used to locate moving
objects. Moving silhouettes like body and face parts can be extracted by simply thresholding accumulated frame
differences [ 20].
In the literature, most commonly used facial feature is distinct side by side appearance of a pair of eyes
[22, 21]. Also, main face axis [24], outline (top of the head) [23, 24], and body (below the head) are searched to
detect face regions. After determining features, which are focus on are searched. According to found features‘
orientations and geometric their geometric ratios, face regions are detected. The facial feature extraction
algorithm by De silva et al. [25] is a good example of feature searching method.
III. UNSUPERVISED CLASSIFICATION BASED APPROACHES FOR FACE RECOGNITION
This section discusses the techniques for face detection that are applicable on most of the frontal faces. The
advantages and disadvantages are presented after each method.
A. Eigen faces based method for face recognition
Also known as Karhunen- Loève expansion, eigenvector, Eigen picture and principal component,
Eigen face is one of the most thoroughly investigated approaches to face recognition. References [26, 27] used
principal component analysis to efficiently represent pictures of faces. They argued that any face images could
be approximately reconstructed by a small collection of weights for each face and a standard face picture (Eigen
picture). The weights describing every single face are obtained by projecting the face image onto the Eigen
picture. Reference [28] used Eigen faces, which was motivated by the technique of Kirby and Sirovich, for face
detection and identification.
In mathematical terms, Eigen faces are the principal components of the eigenvectors of the covariance
matrix, or the distribution of faces of the set of face images. The eigenvectors are asked to represent different
amounts of the variation, respectively, among the faces. Every single face can be represented exactly by a linear
combination of the Eigen faces
As the images include a large quantity of background area, above results are influenced by background.
The authors explained the robust performance of the system under different lighting conditions by significant
correlation between images with changes in illumination. However, [29] showed that the correlation between
images of the whole faces is not efficient for satisfactory recognition performance. Illumination normalization
[27] is usually necessary for the Eigen faces approach.
Reference [30] proposed a new method to compute the covariance matrix using three images each was
taken in different lighting conditions to account for arbitrary illumination effects, if the object is Lambertian.
Reference [31] extended their early work on Eigen face to Eigen features corresponding to face components,
such as eyes, nose, and mouth. They used a modular Eigen space which was composed of the above Eigen
features (i.e., Eigen eyes, Eigen nose, and Eigen mouth). This method would be less sensitive to appearance
changes than the standard Eigen face method. The system achieved a recognition rate of 95 percent on the
FERET database of 7,562 images of approximately 3,000 individuals. In summary, Eigen face appears as a fast,
simple, and practical method. However, in general, it do not provide invariance over changes in scale and
lighting conditions.
B. Dynamic Graph Matching Algorithms for face recognition
Graph matching is another approach to face recognition. Reference [32] is about dynamic link structure
for any distortion invariant object recognition that employs an elastic graph match to find the closest stored
graph. Dynamic link architectures are the extension to classical artificial neural networks. Objects memorized
by sparse graphs have edges labelled with geometrical distance vectors. Stochastic optimization of a matching
cost function can perform elastic graph matching for object recognition. They reported good results on a
database of 87 people and a small set of office items comprising different expressions with a rotation of 15
degrees.
In general terms of rotation invariance, dynamic link architecture is superior to other face recognition
techniques; however, the matching process is computationally expensive.
C. Geometrical Feature Matching for face recognition system
Geometrical feature matching techniques are based on the computation of a set of geometrical features
from the picture of a face. The face recognition is possible on a course of low resolution of 8x6 pixels [33]. It is
hard to determine the single face features which implies that it is sufficient to have overall geometric
6. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 6 | Page
configuration of face features. The vector which represents the position and size of main facial features can
describe the overall configuration such as eyes and eyebrows, mouth, nose and the shape of face outline. One of
the pioneering works on automated face recognition by using geometrical features was done by [34] in 1973.
Their system achieved a peak performance of 75% recognition rate on a database of 20 people using two images
per person, one as the test and the other as the model image. References [35, 36] showed that a face recognition
program provided with features extracted manually could perform recognition apparently with satisfactory
results. Reference [37] automatically extracted a set of geometrical features from the picture of a face, such as
mouth position, nose width and length, and chin shape. There were 35 features extracted from a 35 dimensional
vector. Then the recognition was performed with a Bayes classifier. It reported a recognition rate of 90% on a
database of 47 people.
Reference [38] introduced a mixture-distance technique which achieved 95% recognition rate on a
query database of 685 individuals. The recognition accuracy in terms of the best match to the right person was
86% and 94% of the correct person's faces were in the top three candidate matches. In summary, geometrical
feature matching that are based on accurate measured distances between features can be the most appropriate
thing to find possible matches in a large database (for ex: Mug shot album). However, the dependency will be a
factor of accurate feature location algorithms. Conventional automated face feature location algorithm cannot
provide with high degree of accuracy and are also time consuming.
D. Template Matching Algorithms for face recognition
A test image represented as a 2-D array of intensity values compared using a suitable metric represents
the simple version of template matching. For example we can say that with a single template Euclidean distance
represents the whole face. One can use more than one face templates with different viewpoints to represent an
individual's face.
A face from a single viewpoint can also be represented by a set of multiple distinctive smaller
templates [37, 39]. The face image of gray levels may also be properly processed before matching [40]. In [37],
Bruneli and Poggio automatically selected a set of four features templates, i.e., the eyes, nose, mouth, and the
whole face, and for all of the available faces. They compared the performance of their geometrical matching
algorithm and template matching algorithm on the same database of faces which contains 188 images of 47
individuals. The template matching was superior in recognition (100 percent recognition rate) to geometrical
matching (90 percent recognition rate) and was also simpler. Since the principal components (also known as
Eigen faces or Eigen features) are linear combinations of the templates in the data basis, the technique can never
achieve better results than correlation [37], but it may be less computationally expensive.
One drawback of template matching is its computational complexity. Another problem lies in description of
these templates. Since the recognition system is supposed to be tolerant to certain discrepancies between the
template and the test image, this tolerance may average out the differences that make individual faces unique.
E. Line Edge Map (LEM) based methods for face recognition
Edge information is a useful object representation feature that is insensitive to illumination changes to
certain extent. Though the edge map is widely used in various pattern recognition fields, it was neglected in face
recognition except in recent work reported in [41].
For object recognition Edge images of objects could be used and achieve similar accuracy as gray-level
pictures. In reference [41] edge maps measured the similarity between face images. 92% accuracy was achieved
in the paper. Takács stated that the face recognition process may start at a very early stage and for recognition
process can use edge images without involving the high-level cognitive functions.
A Line Edge Map approach, proposed by [10], extracts lines from a face edge map as features. This
approach can be considered as a combination of template matching and geometrical feature matching. The LEM
approach not only have the advantages of feature based approaches, such as invariance to illumination and low
memory requirement, but also has the advantage of high recognition performance of template matching.
IV. SUPERVISED LEARNING BASED APPROACHES FOR FACE RECOGNITION
A. Neural Network based methods for face recognition
For face verification, [43] is a multi-resolution pyramid structure. Reference [42] proposed a hybrid
neural network which combines a self-organizing map (SOM) neural network, local image sampling, and a
convolutional neural network. The quantization of the image samples into a topological space is provided by
SOM where inputs of original space and output space are nearby only and hence provide dimension reduction
and invariance to negligible changes in the sample.
The authors reported 96.2% correct recognition on ORL database of 400 images of 40 individuals.
The classification time is less than 0.5 second, but the training time is as long as 4 hours. Reference
[44] used probabilistic decision-based neural network (PDBNN) which inherited the modular structure from its
7. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 7 | Page
predecessor, a decision based neural network (DBNN) [45]. The PDBNN can be applied effectively to 1) face
detector: which finds the location of a human face in a cluttered image, 2) eye localizer: which determines the
positions of both eyes in order to generate meaningful feature vectors, and 3) face recognizer. PDNN. The
learning scheme of the PDNN consists of two phases, in the first phase; each subnet is trained by its own face
images. In the second phase, called the decision-based learning, the subnet parameters may be trained by some
particular samples from other face classes. The decision-based learning scheme does not use all the training
samples for the training. Only misclassified patterns are used. If the sample is misclassified to the wrong subnet,
the rightful subnet will tune its parameters so that its decision-region can be moved closer to the misclassified
sample.
B. Hidden Markov Models (HMMs)based methods for face recognition
Stochastic modeling of non-stationary vector time series based on (HMM) is quite successful for voice
applications. In reference [46] authors applied this methodology for human face recognition. Faces were divided
in parts such as eyes, nose, mouth, etc. which are to be associated with the states of hidden Markov model. Since
only one-dimensional observation sequence and 2-D image sequence are required by HMMs it is required to
convert images either to 1-D spatial sequence or 1-D temporal sequence.
In [47], a spatial observation sequence was extracted from a face image by using a band sampling
technique. Each face image was represented by a 1D vector series of pixel observation. Each observation vector
is a block of L lines and there is an M lines overlap between successive observations. An unknown test image is
first sampled to an observation sequence. Then, it is matched against every HMMs in the model face database
(each HMM represents a different subject). The match with the highest likelihood is considered the best match
and the relevant model reveals the identity of the test face.
C. Support Vector Machine (SVM) based methods for face recognition
SVM is a learning technique that is considered an effective method for general purpose pattern
recognition because of its high generalization performance without the need to add other knowledge [48].
Intuitively, given a set of points belonging to two classes, SVM finds the hyper plane that separates the largest
possible fraction of points of the same class on the similar side, while maximizing the distance from either class
to the hyperplane. According to [48], this hyper plane is called Optimal Separating Hyper plane (OSH) which
minimizes the risk of misclassifying not only the examples in the training set but also the unseen example of the
test set.
In summary, the main characteristics of SVMs are:
1) That they minimize a formally proved upper bound on the generalization error;
2) That they work on high-multi dimensional feature spaces by means of a dual formulation in terms of
kernels;
3) That the prediction is based on hyperplanes in these feature spaces, that correspond to quite involved
classification criteria on the input data; and
4) Those outliers in the training data set can be handled by means of soft margins.
D. Fuzzy Theory Based Approaches for Face Recognition System
This approach detects faces in color images based on the fuzzy theory. [49] is a typical example of fuzzy
detection category. In this paper Wu et al. made two fuzzy models to describe the skin and hair color, in which
used a perceptually uniform color space to describe the color information to increase the accuracy and
stableness. Furthermore, the models were used to extract the skin and hair color regions, then comparing them
with the pre-built head-shape models by using a fuzzy theory based pattern-matching method to detect face
candidates, this part of research is little untouched by researchers and there are only few work that already done
in the field.
This approach uses fuzzy theory to representing diverse, uncertain, non-exact, and inaccurate
knowledge or information. And information carried in individual fuzzy set is combined to make a decision.
Processes of composition and de-fuzzification form the basis of fuzzy reasoning. Fuzzy reasoning is performed
to recognize face in the context of a fuzzy system model that consists of control, solution, and working data
variables; fuzzy sets; hedges; fuzzy; and a control mechanism. Many paradigms are appeared in [50, 51].
V. COMPARISON OF DIFFERENT FACE DATA BASE FOR FACE RECOGNITION
In Section 3 and 4, a number of face recognition algorithms have been described. In order to compare
the performance of various face recognition algorithms presented in the literature there is need for a
comprehensive and systematically annotated database populated with face images that have been captured (1) at
variety of pose angles, (2) with a wide variety of illumination angles (to permit testing of illumination
invariance), and (3) under a variety of commonly encountered illumination color temperatures (Permit testing of
illumination color invariance).
8. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 8 | Page
Reference [52] presents a method that use the novel set of apparatus to rapidly capture face images in a
broad range of pose angles and illumination angles. 4 different types of illumination employed includes the
daylight, skylight, flourescent and incandescent. the annotations and the entire set of images along with the
experimental results are placed in public domain and were made available for download on www [53].
VI. CONCLUSION & DISCUSSION
Face detection and recognition are both the complicated and in its own part necessary for the
recognition techniques. Among all the biometric techniques, face recognition provides friendly features like
user-friendliness and satisfying security level. This paper is the introductory survey for face recognition
technology. Issues such as generic framework for face recognition, some factors affecting the performance of
the recognizer, and several state-of-the-arts face recognition algorithms are studied in this paper.
Face recognition algorithms that were using Description and limitations of face databases are scripted
in this paper. Finally, we give a summary of the research results. This is also shown in tabular form in Table I
and II. This paper contributes to give details about most well-known techniques for face recognition like, Eigen
face based methods, Dynamic Graph Matching Algorithms, Geometrical Feature Matching, Template Matching
Algorithms, Line Edge Map (LEM) based methods, neural network, Hidden Markov model, support vector
machine, Fuzzy theory etc.
Table 1: Comparison of different face databases
Database Description Limitation
AT&T [54]
(formerly ORL)
Contains face images of 40 persons, with 10 images of each.
For most subjects, the 10 images were shot at different times
and with different lighting conditions, but always against a dark
background.
(1) Limited number of people
(2) Illumination conditions are not consistent
from image to image.
(3) The images are not annotated for different
facial expressions, head rotation, or lighting
conditions.
Oulu Physics [55] Includes frontal color images of 125 different faces. Each face
was photographed 16 times, using 1 of 4 different illuminants
(horizon, incandescent, fluorescent, and daylight) in
combination with 1 of 4 different camera calibrations (color
balance settings). The images were captured under dark room
conditions, and a gray screen was placed behind the participant.
The spectral reflectance (over the range from 400 nm to 700
nm) was measured at the forehead, left cheek, and right cheek
of each person with a spectrophotometer. The spectral
sensitivities of the R, G and B channels of the camera, and the
spectral power of the four illuminants were also recorded over
the same spectral range.
(1) Although this database contains images
captured under a good variety of illuminant
colors, and the images are annotated for
illuminant, there are no variations in the lighting
angle.
(2) All of the face images are basically frontal
(with some variations in pose angle and
distance from the camera)
XM2VTS [56] Consists of 1000 GB of video sequences and speech recordings
taken of 295 subjects at one-month intervals over a period of 4
months (4 recording sessions). Significant variability in
appearance of clients (such as changes of hairstyle, facial hair,
shape and presence or absence of glasses) is present in the
recordings. During each of the 4 sessions a ―speech‖ video
sequence and a ―head rotation‖ video sequence was captured.
This database is designed to test systems designed to do
multimodal (video + audio) identification of humans by facial
and voice features.
It does not include any information about the
image acquisition parameters, such as
illumination angle, illumination color, or pose
angle.
Yale [57] Contains frontal grayscale face images of 15 people, with 11
face images of each subject, giving a total of 165 images.
Lighting variations include left-light, center-light, and right-
light. Spectacle variations include with-glasses and without-
glasses. Facial expression variations include normal, happy,
sad, sleepy, surprised, and wink.
(1) Limited number of people
(2) While the face images in this database were
taken with 3 different lighting angles (left,
center, and right) the precise positions of the
light sources are not specified.
(3) Since all images are frontal, there are no
pose angle variations.
(4) Environmental factors (such as the presence
or absence of ambient light) are also not
described.
9. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 9 | Page
Yale B [58] Contains grayscale images of 10 subjects with 64 different
lighting angles and 9 different poses angles, for a total of 5760
images. Pose 0 is a frontal view, in which the subject directs
his/her gaze directly into the camera lens. In poses 1, 2, 3, 4,
and 5 the subject is gazing at 5 points on a semicircle about 12
degrees away from the camera lens, in the left visual field. In
poses 6, 7, and 8 the subject is gazing at 3 different points on a
semicircle about 24 degrees away from the camera lens, again
in the left visual field. The images were captured with an
overhead lighting structure which was fitted with 64 computer
controlled xenon strobe lights. For each pose, 64 images were
captured of each subject at a rate of 30 frames/sec, over a
period of about 2 seconds.
(1) Limited number of Subjects.
(2) The background in these images is not
homogeneous, and is cluttered.
(3) The 9 different pose angles in these images
were not precisely controlled. Where the exact
head orientation (both vertically and
horizontally) for each pose was chosen by the
subject.
MIT [59] Contains 16 subjects. Each subject sat on a couch and was
photographed 27 times, while varying head orientation. The
lighting direction and the camera zoom were also varied during
the sequence. The resulting 480 x 512 grayscale images were
then filtered and sub sampled by factors of 2, to produce six
levels of a binary Gaussian pyramid. The six ―pyramid levels‖
are annotated by an X-by-Y pixel count, which ranged from
480x512 down to 15x16.
(1) Although this database contains images that
were captured with a few different scale
variations, lighting variations, and pose
variations, these variations were not very
extensive, and were not precisely measured.
(2) There was also apparently no effort made to
prevent the subjects from moving between
pictures.
CMU Pose,
Illumination, and
Expression (PIE)
[98]
Contains images of 68 subjects that were captured with 13
different poses, 43 different illumination conditions, and 4
different facial expressions, for a total of 41,368 color images
with a resolution of 640 x 486. Two sets of images were
captured – one set with ambient lighting present, and another
set with ambient lighting absent.
(1) There was clutter visible in the backgrounds
of these images.
(2) The exact pose angle for each image is not
specified.
UMIST [60] Consists of 564 grayscale images of 20 people of both sexes
and various races. (Image size is about 220 x 220.) Various
pose angles of each person are provided, ranging from profile
to frontal views.
(1) No absolute pose angle is provided for each
image.
(2) No information is provided about the
illumination used – either its direction or its
color temperature.
Bern University
face database [61]
Contains frontal views of 30 people. Each person has 10 gray-
level images with different head pose variations (two front
parallel pose, two looking to the right, two looking to the left,
two looking downwards, and two looking upwards). All images
are taken under controlled/ideal conditions.
(1) Limited number of subjects.
(2) The exact pose angle for each image
is not specific.
(3) There is not variation in illumination
conditions.
Purdue AR [62] Contains over 4,000 color frontal view images of 126 people's
faces (70 men and 56 women) that were taken during two
different sessions separated by 14 days. Similar pictures were
taken during the two sessions. No restrictions on clothing,
eyeglasses, make-up, or hair style were imposed upon the
participants. Controlled variations include facial expressions
(neutral, smile, anger, and screaming), illumination (left light
on, right light on, all side lights on), and partial facial
occlusions (sun glasses or a scarf).
The placement of those light sources, the color
temperature of those light sources, and whether
they were diffuse of point light sources is not
specified. (The placement of the two light
sources produces objectionable glare in the
spectacles of some subjects.)
The University of
Stirling online
database
[63]
Was created for use in psychology research, and contains
pictures of faces, objects, drawings, textures, and natural
scenes. A web-based retrieval system allows a user to select
from among the 1591 face images of over 300 subjects based
on several parameters, including male, female, grayscale, color,
profile view, frontal view, or 3/4 view.
(1) No information is provided about the
illumination used during the image capture.
(2) Most of these images were also captured in
front of a black background, making it difficult
to discern the boundaries of the head of those
subjects with dark hair.
The FERET [64] Contains face images of over 1000 people. It was created by
the FERET program, which ran from 1993 through 1997. The
database was assembled to support government monitored
testing and evaluation of face recognition algorithms using
standardized tests and procedures. The final set of images
consists of 14051 grayscale images of human heads with views
that include frontal views, left and right profile views, and
quarter left and right views. It contains many images of the
same people taken with time-gaps of one year or more, so that
some facial features have changed. This is important for
evaluating the robustness of face recognition algorithms over
time.
(1) It does not provide a very wide variety of
pose variations.
(2) There is no information about the lighting
used to capture the images.
Kuwait University
face database
(KUFDB )
[65]
The in-house built database consists of 250 face acquired from
50 people with five images per face. There is a total 250 gray
level images (5 images x 50 people). Facial images are
normalized to sizes 24 x 24, 32 x 32, and 64 x 64). Images
were acquired without any control of the laboratory
illumination.
Variations in lighting, facial expression, size, and rotation, are
(1) Limited number of people.
(2) It does not include any information about
image acquisition parameter, such as pose
angle.
10. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 10 | Page
considered.
Table 2: Summary of the research results
Database References Method Notes
FERET [31] Eigen features This method would be less sensitive to appearance changes than standard
Eigen face method. The DB contained 7,562 images of approximately
3,000 individuals.
[28] Eigen face DB contained 2,500 images of 16 individuals; the images include a large
quality of background area.
[47] Graph matching
[55] Geometrical feature matching
and Template matching
These two matching algorithms occurred on the same DB which contained
188 images of 47 individuals.
[38] SVM
[75] SVM + 3D morphable model Face rotation up to ±360
in depth.
[76] SVM+PC+LD DB contained 295 people.
AR [10] LEM DB contained frontal faces under controlled condition.
[78] SVM+PCA SVM+ICA SVM was used only with polynomial (up to degree 3) and Gaussian
kernel.
Yale [78] SVM+PCA SVM+ICA DB contained 165 images of 15 individuals. The DB divided into 90
images (6 for each person) for training and 75 for testing (5 for each
person)
[86] Build face recognition
committee machine (FRCM) of
Eigen face, Fisher face, Elastic
Graph Matching (EGM), SVM,
and Neural network
1) They adopt leaving-one-out cross validation method.
2) Without the lighting variations, FRCM achieves 97.8%
accuracy.
[87] Combines holistic and feature
analysis-based approaches using
a
Markov random field (MRF)
method
They tested the recognition accuracy with different numbers of training
samples. K(k=1,2,…10) images of each subject were randomly selected
for training and the remaining 11-k images for testing
[88] Boosted parameter-based
combined classifier
The DB is divided into 75 images (5 for each person) for training and 90
for testing (6 for each person)
ORL [42] Hybrid NN: SOM + a
convolution NN
DB contained 400 images of 40 individuals. The classification time less
than .5 second for recognizing one facial image, but training time is 4
hours.
[47] Hidden Markov model (HMMs)
[47] A pseudo 2DHMM Its classification time and training time were not given (believe to be very
expensive.)
[81] SVM with a binary tree They compare the SVMs with standard Eigen face approach using the
NCC
[77] Optimal-Pairwise coupling (O-
PWC) SVM
They select 200 samples (5 for each individual) randomly as training set.
The remaining 200 samples are used as the test set.
[80] Several SVM+NN
arbitrator
An average processing time of .22 second for face pattern with 40 classes.
On the same DB, PCC for Eigen faces is 90% and for pseudo-2D HMM
is 95% and for convolutional NN is 96.2%
[28] Eigen face
[44] PDBNN PDBNN face recognizing up to 200 people in approximately 1 second and
the training time is 20 minutes.
11. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 11 | Page
[85] A combined classifier uses the
generalization capabilities of
both Learning Vector
Quantization (LVQ) and Radial
Basis Function (RBF) neural
networks to build a
representative model of a face
from a variety of training
patterns with different poses,
details and facial expressions
A new face synthesis method is implemented for reducing the false
acceptance rate and enhancing the rejection capability of the classifier.
The system is capable of recognizing a face in less than one second.
References And Allusions
[1] Ming-Hsuan Yang, David J. Kriegman, Narendra Ahuja, ―Detecting Faces in Images: A Survey‖, IEEE Trans. PAMI, vol. 24,
No.1, pp. 1-25, Jan 2002.
[2] Craw, H. Ellis, and J. R. Lishman, Automatic extraction of face-feature, Pattern Recog. Lett. Feb. 1987, 183–187.
[3] H. Graf, E. Cosatto, and T. Ezzat, Face analysis for the synthesis of photo-realistic talking heads, in Proceedings Fourth IEEE
International Conference on Automatic Face and Gesture Recognition, 2000.
[4] H. P. Graf, T. Chen, E. Petajan, and E. Cosatto, Locating faces and facial parts, in IEEE Proc. of Int. WorkshoponAutomatic Face-
and Gesture-Recognition, Zurich, Switzerland, Jun. 1995, pp. 41– 45.
[5] Sudipta Roy ,Prof. Samir K. Bandyopadhyay, ―Detection and Quantification of Brain Tumor from MRI of Brain and it‗s
Symmetric Analysis‖ ,International Journal of Information and Communication Technology Research(IJICTR), pp. 477-
483,Volume 2, Number 6, June 2012. [ISSN : 2223-4985]
[6] Ming-Hsuan Yang, David J. Kriegman, Narendra Ahuja, ―Detecting Faces in Images: A Survey‖, IEEE Trans. PAMI, vol. 24,
No.1, pp. 1-25, Jan 2002.
[7] H. P. Graf, E. Cosatto, D. Gibson, E. Petajan, and M. Kocheisen, Multi-modal system for locating heads and faces, in IEEE Proc. of
2nd Int. Conf. on Automatic Face and Gesture Recognition, Vermont, Oct. 1996, pp. 277–282.
[8] Sudipta Roy, Prof. Samir K. Bandyopadhyay, ―Contour Detection of Human Knee‖, International Journal of Computer Science
Engineering and Technology (IJCSIT) ,September 2011 , Vol 1, Issue 8,pp. 484-487.
[9] Govindaraju, Locating human faces in photographs, Int. J. Comput. Vision 19, 1996.
[10] Y. Gao and K.H. Leung, ―Face recognition using line edge map,‖ IEEE Transactions on Pattern Analysis and Machine Intelligence,
vol. 24, no. 6, June 2002.
[11] L. G. Frakas and I. R. Munro, Anthropometric Facial Proportions in Medicine. Charles C. Thomas, Spring- field, IL, 1987.
[12] M. Hunke, A. Waibel, "Face Locating and Tracking for Human-computer Interaction".
[13] S. McKenna, S. Gong, J. J. Collins, "Face Tracking and Pose Representation", 1997.
[14] J. Yang, A. Waibel, "A Real Time Face Tracker", 1996
[15] J. L. Crowley, F. Berard, "Multi-model Tracking of faces for Video Communications".
[16] S. Kawato, J. Ohya, "Real-Time Detection of Nodding and Head-Shaking by Directly Detecting and Tracking The "Between-Eyes"
", 2000.
[17] Q. B. Sun, W. M. Huang, J. K. Wu, "Face Detection Based on Color and Local Symetry Information".
[18] K. Yachi, T. Wada, T. Matsuyama, "Human Head Tracking using Adaptive Appearance Models with a Fixed View Point Pantilt-
Zoom-Camera".
[19] B. K. Low, M. K. Ibrahim, "A Fast and Accurate Algorithm Facial Feature Segmentation", 1997.
[20] M J. T. Rainders, P. J. L. van Beek, B. Sankur, J. C. A. van der Lubbe, "Facial Feature Localization and Adaptation of a Generic
Face Model for ModelBased Coding", 1994.
[21] H. P. Graf, E. Cosatto, D. Gibson, E. Petajan, and M. Kocheisen, Multi-modal system for locating heads and faces, in IEEE Proc. of
2nd Int. Conf. on Automatic Face and Gesture Recognition, Vermont, Oct. 1996, pp. 277–282.
[22] T. Sakai, M. Nagao, and T. Kanade, Computer analysis and classification of photographs of human faces, in Proc. First USA—
Japan Computer Conference, 1972, p. 2.7.
[23] Craw, H. Ellis, and J. R. Lishman, Automatic extraction of face-feature, Pattern Recog. Lett. Feb. 1987, 183–187.
[24] L. C. De Silva, K. Aizawa, and M. Hatori, Detection and tracking of facial features by using a facial feature model and deformable
circular template, IEICE Trans. Inform. Systems E78–D(9), 1995, 1195 1207.
[25] L. Sirovich and M. Kirby, ―Low-Dimensional procedure for the characterisation of human faces,‖ J. Optical Soc. of Am., vol. 4, pp.
519-524, 1987.
[26] M. Kirby and L. Sirovich, ―Application of the Karhunen- Loève procedure for the characterisation of human faces,‖ IEEE Trans.
Pattern Analysis and Machine Intelligence, vol. 12, pp. 831-835, Dec. 1990.
[27] M. Turk and A. Pentland, ―Eigenfaces for recognition,‖ J. Cognitive Neuroscience, vol. 3, pp. 71-86, 1991.
[28] M.A. Grudin, ―A compact multi-level model for the recognition of facial images,‖ Ph.D. thesis, Liverpool John Moores Univ.,
1997.
[29] L. Zhao and Y.H. Yang, ―Theoretical analysis of illumination in pcabased vision systems,‖ Pattern Recognition, vol. 32, pp. 547-
564, 1999.
[30] Pentland, B. Moghaddam, and T. Starner, ―View-Based and modular eigenspaces for face recognition,‖ Proc. IEEE CS Conf.
Computer Vision and Pattern Recognition, pp. 84-91, 1994.
[31] M. Lades, J.C. Vorbruggen, J. Buhmann, J. Lange, C. Von Der Malsburg, R.P. Wurtz, and M. Konen, ―Distortion Invariant object
recognition in the dynamic link architecture,‖ IEEE Trans. Computers, vol. 42, pp. 300-311, 1993.
[32] S. Tamura, H. Kawa, and H. Mitsumoto, ―Male/Female identification from 8_6 very low resolution face images by neural network,‖
Pattern Recognition, vol. 29, pp. 331-335, 1996.
[33] T. Kanade, ―Picture processing by computer complex and recognition of human faces,‖ technical report, Dept. Information Science,
Kyoto Univ., 1973.
[34] A.J. Goldstein, L.D. Harmon, and A.B. Lesk, ―Identification of human faces,‖ Proc. IEEE, vol. 59, pp. 748, 1971.
[35] Y. Kaya and K. Kobayashi, ―A basic study on human face recognition,‖ Frontiers of Pattern Recognition, S. Watanabe, ed., pp. 265,
1972.
[36] R. Bruneli and T. Poggio, ―Face recognition: features versus templates,‖ IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 15, pp. 1042-1052, 1993.
12. Techniques for Face Detection & Recognition System- a Comprehensive Review
www.iosrjournals.org 12 | Page
[37] I.J. Cox, J. Ghosn, and P.N. Yianios, ―Feature-Based face recognition using mixture-distance,‖ Computer Vision and Pattern
Recognition, 1996.
[38] B.S. Manjunath, R. Chellappa, and C. von der Malsburg, ―A Feature based approach to face recognition,‖ Proc. IEEE CS Conf.
Computer Vision and Pattern Recognition, pp. 373-378, 1992.
[39] R.J. Baron, ―Mechanism of human facial recognition,‖ Int'l J. Man Machine Studies, vol. 15, pp. 137-178, 1981.
[40] M. Bichsel, ―Strategies of robust object recognition for identification of human faces,‖ Ph.D. thesis,
EidgenossischenTechnischenHochschule, Zurich, 1991.
[41] B. Takács, ―Comparing face images using the modified hausdorff distance,‖ Pattern Recognition, vol. 31, pp. 1873-1881, 1998.
[42] S. Lawrence, C.L. Giles, A.C. Tsoi, and A.D. Back, ―Face recognition: A convolutional neural-network approach,‖ IEEE Trans.
Neural Networks, vol. 8, pp. 98-113, 1997.
[43] J. Weng, J.S. Huang, and N. Ahuja, ―Learning recognition and segmentation of 3D objects from 2D images,‖ Proc. IEEE Int'l Conf.
Computer Vision, pp. 121-128, 1993.
[44] S.H. Lin, S.Y. Kung, and L.J. Lin, ―Face recognition/detection by probabilistic decision-based neural network,‖ IEEE Trans. Neural
Networks, vol. 8, pp. 114-132, 1997.
[45] S.Y. Kung and J.S. Taur, ―Decision-Based neural networks with signal/image classification applications,‖ IEEE Trans. Neural
Networks, vol. 6, pp. 170-181, 1995.
[46] Samaria and F. Fallside, ―Face identification and feature extraction using hidden markov models,‖ Image Processing: Theory and
Application, G. Vernazza, ed., Elsevier, 1993.
[47] Samaria and A.C. Harter, ―Parameterisation of a stochastic model for human face identification,‖ Proc. Second IEEE Workshop
Applications of Computer Vision, 1994.
[48] V.N. Vapnik, ―The nature of statistical learning theory,‖ New York: Springverlag, 1995.
[49] Haiuan, W.—Qian, Ch.—Yachida, M.: Face Detection from Color Image Usinga Fuzzy Pattern Matching Method. IEEE
Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, 1999, No. 6, pp. 557–563.
[50] Mirhosseini, A. R.—Hong, Y.: Human Face Recognition: An Evidence Aggregation Approach. Computer Vision and Image
Understanding, Vol. 71, 1998, No. 2, pp. 213–230.
[51] Gyu-Tae, P.—Bien, Z.: Neural Network-Based Fuzzy Observer with Applicationto Facial Analysis. Pattern Recognition Letters,
Vol. 21, 2000, No. 1, pp. 93–105.
[52] John A. Black, M. Gargesha, K. Kahol, P. Kuchi, SethuramanPanchanathan,‖ A Framework for performance evaluation of face
recognition algorithms,‖ in Proceedings of the International Conference on ITCOM, Internet Multimedia Systems II, 2002.
[53] The At & T Database of Faces , Available: http://www.uk.research.att.com/facedatabase.html
[54] The Oulu Physics database, Available: http://www.ee.oulu.fi/research/imag/color/pbfd.html
[55] The XM2VTS database, Available: http://www.ee.surrey.ac.uk/Reseach/VSSP/xm2vtsdb/
[56] The Yale database, Available: http://cvc.yale.edu /
[57] The Yale B database, Available: http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html
[58] The MIT face database, Available: ftp://whitehapel.media.mit.edu/pub/images/
[59] The CMU PIE database, Available: http://www.ri.cmu.edu/projects/project_418.html
[60] The UMIST database, Available: http://images.ee.umist.ac.uk/danny/database.html
[61] Bern Univ. Face Database, ftp://iamftp.unibe.ch/ pub/Images/FaceImages/, 2002.
[62] Purdue Univ. Face Database, http://rvl1.ecn.purdue.edu/~aleix/aleix_face_DB.html , 2002.
[63] The University of Stirling online database , Available: http://pics.psych.stir.ac.uk/
[64] The FERET database, Available: http://www.it1.nist.gov/iad/humanid/feret/
[65] Kuwait University Face Database, Available: http://www.sc.kuniv.edu.kw/lessons/9503587/dina.htm
[66] L. Wiskott and C. von der Malsburg, ―Recognizing faces by dynamic link matching,‖ Neuroimage, vol. 4, pp. 514-518, 1996.
[67] J. Huang, V. Blanz, and B. Heisele, ―Face recognition using Component-Based support vector machine Classification and
Morphable models,‖ LNCS 2388, pp. 334-341, 2002.
[68] K.Jonsson, J. Mates, J. Kittler and Y.P. Li, ―Learning support vectors for face verification and recognition,‖ Fourth IEEE
International Conference on Automatic Face and Gesture Recognition 2000, pp. 208-213, Los Alamitos, USA, March 2000.
[69] O. Deniz, M. Castrillon, M. Hernandez, ―Face recognition using independent component analysis and support vector machines,‖
Pattern Recognition Letters, vol. 24, pp. 2153-2157, 2003.
[70] Ho-Man Tang, Michael Lyu, and Irwin King, "Face recognition committee machine," In Proceedings of IEEE International
Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003), pp. 837-840, April 6-10, 2003.
[71] Rui Huang, Vladimir Pavlovic, and Dimitris N. Metaxas, "A hybrid face recognition method using Markov random fields," ICPR
(3) , pp. 157-160, 2004.
[72] A.S. Tolba, A.H. El-Baz, and A.A. El-Harby, "A robust boosted parameter- based combined classifier for pattern recognition,"
submitted for publication.
[73] K. Jonsson, J. Kittler, Y. P. Li, and J. Matas, ―Support vector machines for face authentication,‖ in T. Pridmore and D. Elliman,
editors, British Machine Vision Conference, pp. 543–553, 1999.
[74] G.D. Guo, H.J. Zhang, S.Z. Li. "Pairwise face recognition". In Proceedings of 8th IEEE International Conference on Computer
Vision. Vancouver, Canada. July 9-12, 2001.
[75] K. I. Kim, K. Jung, and J. Kim, ―Face recognition using support vector machines with local correlation kernels,‖ International
Journal of Pattern Recognition and Artificial Intelligence, vol. 16 no. 1, pp. 97-111, 2002.
[76] A.S. Tolba, and A.N. Abu-Rezq, "Combined classifiers for invariant face recognition," Pattern Anal. Appl. Vol. 3, no. 4, pp. 289-
302, 2000.