Muhammad Gulraj has a BS in computer system engineering from GIKI, Pakistan and an MS in computer system engineering from UET Peshawar, Pakistan. The document discusses pattern recognition, which involves taking decisions based on input data patterns. It describes common pattern recognition techniques like classification, regression, supervised and unsupervised learning. It outlines applications in security, medical diagnosis, search engines, data mining, speech recognition, robotics, and astronomy. The basic steps are data acquisition, pre-processing, feature extraction, classification, and decision making. Research opportunities exist in improved feature extraction, classification, and applications like human identification, medical diagnosis, and robotics.
A review on fake biometric detection system for various applicationseSAT Journals
Abstract Now a days Security is a major concern for Scenario. So many securities are available but it should be reliable. A biometric system is a computer system which is related to the human characteristic. It is mainly used in identification and access control on their behavioral and physiological category. For example signature, voice, retina, key stroke, face, iris and fingerprint etc. This paper introduces a software base multi attack protection method which is based on various biometric modalities such as iris, face, signature and hand palm image.. This Hand palm technique is used for physical access. The real and fake images are identified by using image quality assessment (IQA) technique. Fake identities always have some different feature than original such as sharpness, different color, information quality etc. In this paper, liveness detection method is used. Which provide a very good performance and low degree of complexity. Also quality of Image is using two methods Full- Reference (FR) and No-Reference (NR). This image quality assessment (IQA) method is suitable for real time application which has been used for very low complexity. Keyword: Statistical Feature Extraction Biometric, Attack, Image Quality Assessment, Full-reference IQA, NO-Reference IQA,.
IMAGE QUALITY ASSESSMENT FOR FAKE BIOMETRIC DETECTION: APPLICATION TO IRIS, F...ijiert bestjournal
In this Paper,the actual presence of a real legitimate trait in contrast to a fake self - manufactured synthetic or reconstructed sample is a significant problem in biometric authentication,which requires the development of new and efficient protection measures. In this paper,we present a novel software - based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The obje ctive of the proposed system is to enhance the security of biometric recognition frameworks,by adding livens assessment in a fast,user - friendly,and non - intrusive manner,through the use of image quality assessment. The proposed approach presents a very low degree of complexity,which makes it suitable for real - time applications,using 25 general image quality features extracted from one image (i.e.,the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results,obtained on publicly available data sets of fingerprint,iris,and 2D face,show that the proposed method is highly competitive compared with other state - of - the - art approaches and that the analysis of the general image quality of rea l biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
A REVIEW ON BLIND STILL IMAGE STEGANALYSIS TECHNIQUES USING FEATURES EXTRACTI...IJCSEIT Journal
Steganography is the technique for hiding secret information in other data such as still, multimedia
images, text, audio. Whereas Steganalysis is the reverse technique in which detection of the secret
information is done in the stego image. Steganalysis can be classified on the basis of the techniques used
classified statistical techniques, pattern classification techniques and visual detection techniques .All the
existing techniques can be broadly classified on the basis of the information required for the designing of
the steganalysis. They are targeted and blind steganalysis In targeted technique, we first look at
steganalysis techniques is designed for a particular steganographic embedding algorithm in mind whereas
in blind steganalysis is general class of steganalysis techniques which can be implemented with any
steganographic embedding algorithm, even an unknown algorithm. In this paper, an extensive review
report is presented chronologically on the Blind Image Steganalysis for the still stego images using the
classification techniques.
Improving the accuracy of fingerprinting system using multibiometric approachIJERA Editor
Biometric technology is a science that used to verify or identify the individual based on physical and/or
behavioral traits. Although biometric systems are considered more secure than other traditional methods such as
password, or key, they also have many limitations such as noisy image, or spoof attack. One of the solutions to
overcome these limitations, is by applying a multibiometric system. Multibiometric system has a significant
effect in improving the performance of both security and accuracy of the system. It also can alleviate the spoof
attacks and reduce the fail to enroll error. A multi-sample is one implementations of the multibiometric systems.
In this study, a new algorithm is suggested to provide a second chance for the genuine user who is rejected, to
compare his/her provided finger with the other samples of the same finger. Multisampling fingerprint is used to
implement this new algorithm. The algorithm is activated when the match score of the user is not equal to a
threshold but close to it, then the system provides another chance to compare the finger with another sample of
the same trait. Using multi-sample biometric system improved the performance of the system by reducing the
False Reject Rate (FRR). Applying the original matching algorithm on the presented database produced 3
genuine users, and 5 imposters for the same fingerprint. While after implementing the suggested condition, the
system performance is enhanced by producing 6 genuine users, and 2 imposters for the same fingerprint. This
work was built and executed depending on a previous Matlab code presented by Zhi Li Wu. Thresholds and
Receiver Operating Characteristic (ROC) curves computed before and after implementing the suggested
multibiometric algorithm. Both ROC curves compared. A final decision and recommendations are provided
depending on the results obtained from this project
A Survey of Security of Multimodal Biometric SystemsIJERA Editor
A biometric system is essentially a pattern recognition system being used in adversarial environment. Since,
biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate
data to make the system ineffective by compromising its integrity. Current theory and design methods of
biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of
classical design methods is an open problem to investigate whether they lead to design secure systems. In order
to make biometric systems secure it is necessary to understand and evaluate the threats and to thus develop
effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly,
the extension of theory and design methods of biometric systems is mandatory to safeguard the security and
reliability of biometric systems in adversarial environments.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.
A review on fake biometric detection system for various applicationseSAT Journals
Abstract Now a days Security is a major concern for Scenario. So many securities are available but it should be reliable. A biometric system is a computer system which is related to the human characteristic. It is mainly used in identification and access control on their behavioral and physiological category. For example signature, voice, retina, key stroke, face, iris and fingerprint etc. This paper introduces a software base multi attack protection method which is based on various biometric modalities such as iris, face, signature and hand palm image.. This Hand palm technique is used for physical access. The real and fake images are identified by using image quality assessment (IQA) technique. Fake identities always have some different feature than original such as sharpness, different color, information quality etc. In this paper, liveness detection method is used. Which provide a very good performance and low degree of complexity. Also quality of Image is using two methods Full- Reference (FR) and No-Reference (NR). This image quality assessment (IQA) method is suitable for real time application which has been used for very low complexity. Keyword: Statistical Feature Extraction Biometric, Attack, Image Quality Assessment, Full-reference IQA, NO-Reference IQA,.
IMAGE QUALITY ASSESSMENT FOR FAKE BIOMETRIC DETECTION: APPLICATION TO IRIS, F...ijiert bestjournal
In this Paper,the actual presence of a real legitimate trait in contrast to a fake self - manufactured synthetic or reconstructed sample is a significant problem in biometric authentication,which requires the development of new and efficient protection measures. In this paper,we present a novel software - based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The obje ctive of the proposed system is to enhance the security of biometric recognition frameworks,by adding livens assessment in a fast,user - friendly,and non - intrusive manner,through the use of image quality assessment. The proposed approach presents a very low degree of complexity,which makes it suitable for real - time applications,using 25 general image quality features extracted from one image (i.e.,the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results,obtained on publicly available data sets of fingerprint,iris,and 2D face,show that the proposed method is highly competitive compared with other state - of - the - art approaches and that the analysis of the general image quality of rea l biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
A REVIEW ON BLIND STILL IMAGE STEGANALYSIS TECHNIQUES USING FEATURES EXTRACTI...IJCSEIT Journal
Steganography is the technique for hiding secret information in other data such as still, multimedia
images, text, audio. Whereas Steganalysis is the reverse technique in which detection of the secret
information is done in the stego image. Steganalysis can be classified on the basis of the techniques used
classified statistical techniques, pattern classification techniques and visual detection techniques .All the
existing techniques can be broadly classified on the basis of the information required for the designing of
the steganalysis. They are targeted and blind steganalysis In targeted technique, we first look at
steganalysis techniques is designed for a particular steganographic embedding algorithm in mind whereas
in blind steganalysis is general class of steganalysis techniques which can be implemented with any
steganographic embedding algorithm, even an unknown algorithm. In this paper, an extensive review
report is presented chronologically on the Blind Image Steganalysis for the still stego images using the
classification techniques.
Improving the accuracy of fingerprinting system using multibiometric approachIJERA Editor
Biometric technology is a science that used to verify or identify the individual based on physical and/or
behavioral traits. Although biometric systems are considered more secure than other traditional methods such as
password, or key, they also have many limitations such as noisy image, or spoof attack. One of the solutions to
overcome these limitations, is by applying a multibiometric system. Multibiometric system has a significant
effect in improving the performance of both security and accuracy of the system. It also can alleviate the spoof
attacks and reduce the fail to enroll error. A multi-sample is one implementations of the multibiometric systems.
In this study, a new algorithm is suggested to provide a second chance for the genuine user who is rejected, to
compare his/her provided finger with the other samples of the same finger. Multisampling fingerprint is used to
implement this new algorithm. The algorithm is activated when the match score of the user is not equal to a
threshold but close to it, then the system provides another chance to compare the finger with another sample of
the same trait. Using multi-sample biometric system improved the performance of the system by reducing the
False Reject Rate (FRR). Applying the original matching algorithm on the presented database produced 3
genuine users, and 5 imposters for the same fingerprint. While after implementing the suggested condition, the
system performance is enhanced by producing 6 genuine users, and 2 imposters for the same fingerprint. This
work was built and executed depending on a previous Matlab code presented by Zhi Li Wu. Thresholds and
Receiver Operating Characteristic (ROC) curves computed before and after implementing the suggested
multibiometric algorithm. Both ROC curves compared. A final decision and recommendations are provided
depending on the results obtained from this project
A Survey of Security of Multimodal Biometric SystemsIJERA Editor
A biometric system is essentially a pattern recognition system being used in adversarial environment. Since,
biometric system like any conventional security system is exposed to malicious adversaries, who can manipulate
data to make the system ineffective by compromising its integrity. Current theory and design methods of
biometric systems do not take into account the vulnerability to such adversary attacks. Therefore, evaluation of
classical design methods is an open problem to investigate whether they lead to design secure systems. In order
to make biometric systems secure it is necessary to understand and evaluate the threats and to thus develop
effective countermeasures and robust system designs, both technical and procedural, if necessary. Accordingly,
the extension of theory and design methods of biometric systems is mandatory to safeguard the security and
reliability of biometric systems in adversarial environments.
ENHANCED SYSTEM FOR COMPUTER-AIDED DETECTION OF MRI BRAIN TUMORSsipij
The brain images are indicating what condition the brain has. The objective of this research is to design a software that will automatically classifies the brain images to their associated disorders. In order to achieve the objective of this research, a database for training and testing the software of brain images must to be found. In this research we have 105 number of images in data set. In order to differentiate between the classes of those brain images, features had to be extracted from the images. Then, images will be classified into two classes normal and abnormal by using SVM and KNN classifier. The features that were extracted were used in the classification process. The classifiers performed really well, whereas the SVM classifier performed better since its accuracy is 100% on testing set. In the end, the software was successful in separating the two classes.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
Performance Enhancement Of Multimodal Biometrics Using CryptosystemIJERA Editor
Multimodal biometrics means the unification of two or more uni modal biometrics so as to make the system more reliable and secure. Such systems promise better security. This study is a blend of iris and fingerprint recognition technique and their fusion at feature level. Our work comprises of two main sections: feature extraction of both modalities and fusing them before matching and finally application of an encryption technique to enhance the security of the fused template.
The increasing use of distributed authentication architecture
has made interoperability of systems an important issue. Interoperabil ity of systems affects the maturity of the technology and also improves confidence of users in the technology. Biometric systems are not immune to the concerns of interoperability. Interoperability of fingerprint sensors and its effect on the overall performance of the recognition system is an area of interest with a considerable amount of work directed
towards it. This research analyzed effects of interoperability on error rates for fingerprint datasets captured from two optical sensors and a capacitive sensor when using a single commercially available fingerprint
matching algorithm. The main aim of this research was to emulate a
centralized storage and matching architecture with multiple acquisition
stations. Fingerprints were collected from 44 individuals on all three sensors and interoperable False Reject Rates of less than .31% were achieved using two different enrolment strategies.
ADAPTABLE FINGERPRINT MINUTIAE EXTRACTION ALGORITHM BASED-ON CROSSING NUMBER ...IJCSEIT Journal
In this article, a main perspective of developing and implementing fingerprint extraction and matching
algorithms as a part of fingerprint recognition system is focused. First, developing a simple algorithm to
extract fingerprint features and test this algorithm on PC. The second thing is implementing this algorithm
into FPGA devices. The major research topics on which the proposed approach is developing and
modifying fingerprint extraction feature algorithm. This development and modification are using crossing
number method on pixel representation value ’0’. In this new proposed algorithm, it is no need a process
concerning ROI segmentation and no trigonometry calculation. And specially in obtaining their parameters
using Angle Calculation Block avoiding floating points calculation. As this method is local feature that
usually involve with 60-100 minutiae points, makes the template is small in size. Providing FAR, FRR and
EER, performs the performance evaluation of proposed algorithm. The result is an adaptable fingerprint
minutiae extraction algorithm into hardware implementation with 14.05 % of EER, better than reference
algorithm, which is 20.39 % .The computational time is 18 seconds less than a similar method, which takes
60-90 seconds just for pre-processing step. The first step of algorithm implementation in hardware
environment (embedded) using FPGA Device by developing IP Core without using any soft processor is
presented.
AHP validated literature review of forgery type dependent passive image forge...IJECEIAES
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
As we know the fingerprint is unique of every living objects. It is quite difficult to find out the prints.
Usually the Forensics use Fine powder and duct tapes to identify the prints of living object. As powder is
exceptionally muddled, so such molecule can cause loss of information after that examination the information is
coordinated with the system. The proposed system consists of an embedded device in which it consists of ultra
light to glow the fingerprints details. After that we can detect the fingerprint, analysis and it will checks on the
database, and it will return the output after matching. For matching and analysis of the Fingerprint, we will be
using the Algorithm for matching.
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 NOVEL BINNING AND INDEXING APPROACH USING HAND GEOMETRY AND PALM PRINT TO E...ijcsa
This paper proposes a Bio metric identification system for person identification using two bio metric traits
hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify
key points on palm region of hand. Identified key points are used to find hand geometry feature and palm
print Region of interest (ROI). The discriminative palm print features are extracted by applying local
binary descriptor on palm print ROI. In a bio metric identification system the identity corresponding to the
input image (probe) is determined by comparing probe template with the templates of all identities enrolled
in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute.Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.
Fault Detection in Mobile Communication Networks Using Data Mining Techniques...ijcisjournal
A collection of datasets is Big data so that it to be To process huge and complex datasets becomes difficult.
so that using big data analytics the process of applying huge amount of datasets consists of many data
types is the big data on-hand theoretical models and technique tools. The technology of mobile
communication introduced low power ,low price and multi functional devices. A ground for data mining
research is analysis of data pertaining to mobile communication is used. theses mining frequent patterns
and clusters on data streams collaborative filtering and analysis of social network. The data analysis of
mobile communication has been often used as a background application to motivate many technical
problem in data mining research. This paper refers in mobile communication networking to find the fault
nodes between source to destination transmission using data mining techniques and detect the faults using
outliers. outlier detection can be used to find outliers in multivariate data in a simple ensemble way.
Network analysis with R to build a network.
Abstract—Biometric systems are increasingly deployed in networked environment, and issues related to interoperability are bound to arise as single vendor, monolithic architectures become less desirable. Interoperability issues affect every subsystem of the biometric system, and a statistical framework to evaluate interoperability is proposed. The framework was applied to the acquisition subsystem for a fingerprint recognition system and the results were evaluated using the framework. Fingerprints were collected from 100 subjects on 6 fingerprint sensors. The results show that performance of interoperable fingerprint datasets is not easily predictable and the proposed framework can aid in removing unpredictability to some degree.
Facial image classification and searching –a surveyZac Darcy
Recent developments in the area of image mining have shown the way for incredible growth in
extensively large and detailed image databases. The images which are available in these
databases, if checked, can endow with valuable information to the human users. As one of the
most successful applications of image analysis and understanding, fac
e recognition has
recently gained important attention particularly throughout the past many years. Though
tracking and recognizing face objects is a routine task, building such a system is still an active
research. Among several proposed face rec
ognition schemes, shape based approaches are
possibly the most promising ones. This paper provides an overview of various
classification and retrieval methods that were proposed earlier in literature. Also, this paper
provides a margina
l summary for future research and enhancements in face detection
Performance Enhancement Of Multimodal Biometrics Using CryptosystemIJERA Editor
Multimodal biometrics means the unification of two or more uni modal biometrics so as to make the system more reliable and secure. Such systems promise better security. This study is a blend of iris and fingerprint recognition technique and their fusion at feature level. Our work comprises of two main sections: feature extraction of both modalities and fusing them before matching and finally application of an encryption technique to enhance the security of the fused template.
The increasing use of distributed authentication architecture
has made interoperability of systems an important issue. Interoperabil ity of systems affects the maturity of the technology and also improves confidence of users in the technology. Biometric systems are not immune to the concerns of interoperability. Interoperability of fingerprint sensors and its effect on the overall performance of the recognition system is an area of interest with a considerable amount of work directed
towards it. This research analyzed effects of interoperability on error rates for fingerprint datasets captured from two optical sensors and a capacitive sensor when using a single commercially available fingerprint
matching algorithm. The main aim of this research was to emulate a
centralized storage and matching architecture with multiple acquisition
stations. Fingerprints were collected from 44 individuals on all three sensors and interoperable False Reject Rates of less than .31% were achieved using two different enrolment strategies.
ADAPTABLE FINGERPRINT MINUTIAE EXTRACTION ALGORITHM BASED-ON CROSSING NUMBER ...IJCSEIT Journal
In this article, a main perspective of developing and implementing fingerprint extraction and matching
algorithms as a part of fingerprint recognition system is focused. First, developing a simple algorithm to
extract fingerprint features and test this algorithm on PC. The second thing is implementing this algorithm
into FPGA devices. The major research topics on which the proposed approach is developing and
modifying fingerprint extraction feature algorithm. This development and modification are using crossing
number method on pixel representation value ’0’. In this new proposed algorithm, it is no need a process
concerning ROI segmentation and no trigonometry calculation. And specially in obtaining their parameters
using Angle Calculation Block avoiding floating points calculation. As this method is local feature that
usually involve with 60-100 minutiae points, makes the template is small in size. Providing FAR, FRR and
EER, performs the performance evaluation of proposed algorithm. The result is an adaptable fingerprint
minutiae extraction algorithm into hardware implementation with 14.05 % of EER, better than reference
algorithm, which is 20.39 % .The computational time is 18 seconds less than a similar method, which takes
60-90 seconds just for pre-processing step. The first step of algorithm implementation in hardware
environment (embedded) using FPGA Device by developing IP Core without using any soft processor is
presented.
AHP validated literature review of forgery type dependent passive image forge...IJECEIAES
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
As we know the fingerprint is unique of every living objects. It is quite difficult to find out the prints.
Usually the Forensics use Fine powder and duct tapes to identify the prints of living object. As powder is
exceptionally muddled, so such molecule can cause loss of information after that examination the information is
coordinated with the system. The proposed system consists of an embedded device in which it consists of ultra
light to glow the fingerprints details. After that we can detect the fingerprint, analysis and it will checks on the
database, and it will return the output after matching. For matching and analysis of the Fingerprint, we will be
using the Algorithm for matching.
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 NOVEL BINNING AND INDEXING APPROACH USING HAND GEOMETRY AND PALM PRINT TO E...ijcsa
This paper proposes a Bio metric identification system for person identification using two bio metric traits
hand geometry and palm print. The hand image captured from digital camera is preprocessed to identify
key points on palm region of hand. Identified key points are used to find hand geometry feature and palm
print Region of interest (ROI). The discriminative palm print features are extracted by applying local
binary descriptor on palm print ROI. In a bio metric identification system the identity corresponding to the
input image (probe) is determined by comparing probe template with the templates of all identities enrolled
in biometric system (gallery). Response time to establish the identity of an individual increases in proportion to the number of enrollees. One way to reduce the response time is to retrieve a smaller set of candidate identity templates from the database for explicit comparison. In this paper we propose a coarseto-fine hierarchical approach to retrieve a smaller set of candidate identities called as candidate set to reduce the response time. The proposed approach is tested on the database collected at our institute.Proposed approach is of significance since hand geometry and palm print features can be extracted from the palm region of the hand. Also performance of identification system is enhanced by reducing the response time without compromising the identification accuracy.
Fault Detection in Mobile Communication Networks Using Data Mining Techniques...ijcisjournal
A collection of datasets is Big data so that it to be To process huge and complex datasets becomes difficult.
so that using big data analytics the process of applying huge amount of datasets consists of many data
types is the big data on-hand theoretical models and technique tools. The technology of mobile
communication introduced low power ,low price and multi functional devices. A ground for data mining
research is analysis of data pertaining to mobile communication is used. theses mining frequent patterns
and clusters on data streams collaborative filtering and analysis of social network. The data analysis of
mobile communication has been often used as a background application to motivate many technical
problem in data mining research. This paper refers in mobile communication networking to find the fault
nodes between source to destination transmission using data mining techniques and detect the faults using
outliers. outlier detection can be used to find outliers in multivariate data in a simple ensemble way.
Network analysis with R to build a network.
Abstract—Biometric systems are increasingly deployed in networked environment, and issues related to interoperability are bound to arise as single vendor, monolithic architectures become less desirable. Interoperability issues affect every subsystem of the biometric system, and a statistical framework to evaluate interoperability is proposed. The framework was applied to the acquisition subsystem for a fingerprint recognition system and the results were evaluated using the framework. Fingerprints were collected from 100 subjects on 6 fingerprint sensors. The results show that performance of interoperable fingerprint datasets is not easily predictable and the proposed framework can aid in removing unpredictability to some degree.
Analysis of Feature Selection Algorithms (Branch & Bound and Beam search)Parinda Rajapaksha
Branch & Bound and Beam search algorithms were illustrated according to the feature selection domain. Presentation is structured as follows,
- Motivation
- Introduction
- Analysis
- Algorithm
- Pseudo Code
- Illustration of examples
- Applications
- Observations and Recommendations
- Comparison between two algorithms
- References
Understanding The Pattern Of RecognitionRahul Bedi
Pattern recognition is identifying patterns and regularities in data through algorithms and mathematical models. It’s a field that has revolutionized the way we process and make decisions based on data. Contact EnFuse Solutions today and discover how pattern recognition can transform your business. For more information visit here: https://www.enfuse-solutions.com/
Pattern recognition involves the identification of recurring trends or structures within a given dataset, enabling us to recognize similarities and make predictions. They provide insights into underlying concepts and facilitate informed decision-making based on observed regularities. In machine learning, pattern recognition employs advanced algorithms to detect and analyze regularities within data. This field has wide-ranging applications, particularly in technical domains such as computer vision, speech recognition, and face recognition. Pattern recognition utilizes statistical information, historical data, and the system’s memory to recognize and classify events or entities.
One key attribute of pattern recognition is the ability to learn from data. It leverages available data to improve its performance continually. ML adapts and refines its algorithms through training and iterative processes, enhancing the accuracy and efficiency of pattern recognition. For instance, in the context of recommending books or movies, if a user consistently prefers black comedies, machine learning algorithms can recognize this pattern and suggest similar genre preferences, avoiding suggestions that do not align with the established pattern.
Robotic process automation (RPA) is the application of technology that allows...shailajawesley023
Robotic process automation (RPA) is the application of technology that allows employees in a company to configure computer software or a “robot” to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. Robotic Process Automation, or RPA, describes the application of technology that “allows employees in a company to configure computer software or a ‘robot’ to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems,” according to the Institute for Robotic Process Automation and Artificial Intelligence (IRPAAI).
Any company that uses labor on a large scale for general knowledge process work, where people are performing high-volume, highly transactional process functions, will boost their capabilities and save money and time with robotic process automation software.
Person identification based on facial biometrics in different lighting condit...IJECEIAES
Technological development is an inherent feature of this time, that reliance on electronic applications in all daily transactions (business management, banking, financial transfers, health, and other important aspects of life). Identifying and confirming identity is one of the complex challenges. Therefore, relying on biological properties gives reliable results. People can be identified in pictures, films, or real-time using facial recognition technology. A face individual is a unique identifying biological characteristic to authenticate them and prevents permits another person to assume that individual’s identity without their knowledge or consent. This article proposes the identification model by facial individual characteristics, based on the deep neural network (DNN). The proposed method extracts the spatial information available in an image, analysis this information to extract the salient features, and makes the identifying decision based on these features. This model presents successful and promising results, the accuracy achieves by the proposed system reaches 99.5% (+/- 0.16%) and the values of the loss function reach 0.0308 over the Pins Face Recognition dataset to identify 105 subjects.
A SURVEY ON DEEP LEARNING METHOD USED FOR CHARACTER RECOGNITIONIJCIRAS Journal
The field of Artificial Intelligence is very fashionable today, especially neural networks that work well in various areas such as speech recognition and natural language processing. This Research Article briefly describes how deep learning models work and what different techniques are used in text recognition. It also describes the great progress that has been made in the field of medicine, the analysis of forensic documents, the recognition of license plates, banking, health and the legal industry. The recognition of handwritten characters is one of the research areas in the field of artificial intelligence. The individual character recognition has a higher recognition accuracy than the complete word recognition. The new method for categorizing Freeman strings is presented using four connectivity events and eight connectivity events with a deep learning approach.
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.
Machine learning and pattern recognitionsureshraj43
In a very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Pattern recognition is closely related to artificial intelligence and machine learning. Pattern Recognition is an engineering application of Machine Learning.
During past few years, brain tumor segmentation in CT has become an emergent research area in the field of medical imaging system. Brain tumor detection helps in finding the exact size and location of tumor. An efficient algorithm is proposed in this project for tumor detection based on segmentation and morphological operators. Firstly quality of scanned image is enhanced and then morphological operators are applied to detect the tumor in the scanned image. The problem with biopsy is that the patient has to be hospitalized and also the results (around 15%) give false negative. Scan images are read by radiologist but it's a subjective analysis which requires more experience. In the proposed work we segment the renal region and then classify the tumors as benign or malignant by using ANFIS, which is a non-invasive automated process. This approach reduces the waiting time of the patient.
Information about data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining is a powerful tool that can help you to find patterns and relationships within your data, but data does not work by it self. Now a days huge amount of data is ignored, a survey says that 90% of all the end word is generated in past few years. If we talk about the big data, the data is generated daily is in the form of unstructured data. We were living in the data age where in every place you can see the data generation.
Face Recognition Based Attendance System with Auto Alert to Guardian using Ca...ijtsrd
Now a days the wise attending management system victimization face detection techniques. Daily attending marking could also be a typical and vital activity in colleges and colleges for checking the performance of students. Manual attending maintaining is tough methodology, significantly for large cluster of students. Some machine driven systems developed to x beat these difficulties, have drawbacks like worth, faux attending, accuracy, meddlesomeness. To beat these drawbacks, there is need of good and automatic attending system. We've a bent to unit implementing attending system victimization face recognition. Since face is exclusive identity of person, the problem of pretend attending and proxies could also be resolved. The system uses native binary pattern face recognition technique because it is fast, straightforward and has larger success rate. Also, its pro vision to have an effect on intensity of sunshine draw back and head produce draw back that produces it effective. This wise system could also be degree effective because of maintain the degree will less squat recognition system is planned supported appearance based choices that concentrate on the shortened squatter image rather than native countenance. The remainder step in squatter recognition system is squatter detection Viola Jones squatter detection methodology that capable of method photos terribly whereas achieving higher detection rates is utilized. The complete squatter recognition methodology could also be divided into a pair of parts squatter detection and squatter identification. For face detection, Viola Jones face detection methodology has been used out of the many face detection ways that. Once face detection, face is cropped from the actual image to urge obviate the background. Chemist faces and shear faces ways that are used for face identification. Average photos of subjects area unit used as coaching job set to spice up the accuracy of identification. Diksha Ghare | Prajakta Katakdhod | Shraddha Ujgare | Komal Suskar | Prof. Amruta Surana ""Face Recognition Based Attendance System with Auto Alert to Guardian using Call and SMS"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd23928.pdf
Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/23928/face-recognition-based-attendance-system-with-auto-alert-to-guardian-using-call-and-sms/diksha-ghare
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
The foundations for biometrics or identification systems were laid long ago. Today these developments have contributed to the identification of people, access to private sites and all places that need security and order with the help of computerized computers that perform biometric facial recognition, exclusively based on images of human faces for their function. With the extraction of facial midst characteristics of each person provides information used for the detection of the face. This communication also addresses the different processes, stages and methods of feature extraction operated by facial recognition systems. Including the positive and negative aspects of the implementation of these, the advantages and disadvantages, peoples criteria in this respect. Tovbaev Sirojiddin | Karshiboev Nizomiddin "Image Based Facial Recognition" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31330.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31330/image-based-facial-recognition/tovbaev-sirojiddin
Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objects — and then react to what they “see.”
2. MuhammadGulraj
BS Computersystemengineering, GIKI,Pakistan
MS Computersystemengineering, UETPeshawar,Pakistan
2
Introduction
In real world human beings identify certain things using pattern recognition. It is a built in ability
in human beings, e.g. humans recognize different melodies, faces, words and images using
their innate abilities of pattern recognition.
In computer science Pattern Recognition ‘is taking a decision or inference on the basis of
some input data using the patterns of data’, e.g. an Email system decides on the basis of certain
patterns, whether a specific email is spam or non-spam. Similarly using the patterns of DNA it
can be inferred whether a patient have chances of breast cancer or not.
Input Data Results
Pattern recognition assigns label to input values using classification or regression, which are the
main forms of Pattern recognition. Classification is usually used for discrete class labels in
which certain decisions are made by assigning a label to each input value from the given
classes. Supervised learning is a type of classification because the training set has been
provided. Deciding whether an email is spam or non-spam is an example of
Classification/Supervised learning.
Pattern recognition
system
3. MuhammadGulraj
BS Computersystemengineering, GIKI,Pakistan
MS Computersystemengineering, UETPeshawar,Pakistan
3
The above figure shows an example of Classification. The figure shows that a patient having
small tumor size and young age have less chances of breast cancer (blue color), than the one
having large tumor size and older age (red color). Regression is used for labeling continuous
values.
In Unsupervised learning there is no training set/data provided on which the system can be
trained. Search engines usually implements unsupervised learning algorithms for
indexing/clustering of information. Pattern recognition tries to find the best answer using
statistical and probabilistic variations.
Applications
Pattern recognition has been used in security systems, medical diagnosis, search engines, data
mining and speech recognition systems, optical and hand written character recognition systems,
identification systems, robotics and analysis of astronomical data.
Pattern recognition has been vastly used in Security systems. These systems are normally
used in banks, offices, military and government installations. Some security systems which use
pattern recognition are as follows.
Iris recognition system uses the patterns of iris in human eyes to recognize
individuals. Every human being have unique iris pattern inside his eye on the basis of
which a human being can be recognized.
Face recognition system is another system that uses the patterns of skin and other
features to identify humans in images and videos.
4. MuhammadGulraj
BS Computersystemengineering, GIKI,Pakistan
MS Computersystemengineering, UETPeshawar,Pakistan
4
Another system that is used for security purposes is Finger print recognition system
which uses three different patterns namely whorl, arch and loop of thumb to identify a
person.
Pattern recognition is also used in medical systems to diagnose patients. Systems such as
MRI scanning and computer aided diagnosis systems CAD are using pattern recognition
techniques to identify different diseases.
Search engines e.g. yahoo, Google, Ask and Bing also use pattern recognition techniques to
identify and index information for users.
Data mining is another field in which pattern recognition has been used. Data mining is
basically used for extracting useful information from huge data sets.
Pattern recognition is also used in speech recognition and speech tagging. Speech
recognition system is used for security purposes as well as research in natural language
processing.
Robotics is using pattern recognition to inculcate supervised as well as unsupervised learning
in robots. Scientists are working on super intelligent robots, which can understand the
environment and act accordingly. NASA has developed pattern recognition robots which can be
used for navigating and studying environment of other planets such as Mars. Autonomous and
Semi-autonomous cars have also been introduced using pattern recognition techniques.
Industrial automation uses pattern recognition techniques to identify items that have some
fault from the rest. These techniques have reduced work hours and enhanced efficiency.
Astronomers and scientists use pattern recognition techniques to identify and study different
galaxies. Usually techniques such as decision tree are used for star – galaxy classification.
5. MuhammadGulraj
BS Computersystemengineering, GIKI,Pakistan
MS Computersystemengineering, UETPeshawar,Pakistan
5
Apart from the above mentioned systems, pattern recognition techniques and algorithms are
used in image processing, computer vision, machine learning, artificial intelligence, cognitive
science and psychology.
Basic steps of pattern recognition task
The basic steps that involves in pattern recognition task are
Data acquisition using sensors.
Pre- processing such as segmentation and contrast adjustment.
Feature extraction from the Pre-processed data.
Classification on the basis of features extracted.
Post processing which includes cost to improve system’s performance.
Decision on the basis of the above steps
Data
Acquisition
Pre -
Processing
Feature
Extraction
Classification
Post
Processing
SENSOR
DecisionInput Data
6. MuhammadGulraj
BS Computersystemengineering, GIKI,Pakistan
MS Computersystemengineering, UETPeshawar,Pakistan
6
Techniques
The system usually acquires data using different sensors such as camera, microphone or
scanner. These sensors normally convert analog or continuous data into discrete form so that it
can be used in computer. The data acquired using sensors can be used as input data as well as
a training set for the system.
The second step is Pre-processing. Pre-processing is a process in which the system perform
some operations on the input data to make it useful for the next step. There are several
techniques to make this data useful, e.g. if the input data that is acquired is an image or video
then this image can be divided into different segments or pixels (Segmentation). This image can
also be made useful by adjusting the contrast of the image.
After pre-processing the data is passed to the next step which is called Feature Extraction.
Feature extraction is a domain specific problem and different methods can be applied to extract
features, e.g. in a system that is used for Human recognition, we can extract features such as
shape, hair, legs, skin color, nose, eyes and hands using edge detection, motion detection and
color intensities. Contrast equalization and Gabor filter can be used to extract features from an
image in face recognition system.
The Classification uses the features extracted by the system to assign classes to each point.
The system will have to establish some threshold on the basis of which a certain class can be
assign to every data point. A system can use one or more classifiers depending upon the
problem. There are different classifiers available which can be used such as Bayes classifier,
linear classifiers, nearest neighbor classifier and support vector machines. Bayes classifier is
the most common classifier which is used in classification.
Post processing is usually used for improvement in performance using different techniques
such as minimizing the cost of classification. Post processing can enhance the quality of
decision. Using the above steps the system decides to perform a particular action.
7. MuhammadGulraj
BS Computersystemengineering, GIKI,Pakistan
MS Computersystemengineering, UETPeshawar,Pakistan
7
Areas and application for research
Pattern recognition is a vast and interesting field of study which is interrelated with Artificial
intelligence and machine learning. There are enormous opportunities of research in this field.
Research area includes feature extraction, classification, and discriminant analysis, analysis of
astronomical data using clusters, human identification, image/video analysis, data mining and
business intelligence, speech recognition, optical character recognition OCR, medical diagnosis
systems, industrial automation, autonomous vehicles, robotics and error/cost estimation.
Robotics is another field which uses pattern recognition extensively. Rover 1 which has been
recently sent to Mars by NASA to identify and navigate the environment uses Pattern
recognition. Robots can also be used for fire-fighting. Advance research is going on in robotics
as well.
Medical and cognitive science is another field in which application development and research
has been done using pattern recognition. In cognitive science patterns of human behavior and
their actions according to these patterns are studied. Psychologists use these patterns to
identify human behavioral disorders. Medical diagnosis decision support system is another field
of medical/pattern recognition in which research is carried out.
Pattern recognition is an important subject of research in security systems. There are many
security systems which uses pattern recognition techniques. But these systems have also some
hacks, so scientists are constantly trying to develop new algorithms and techniques to find new
and enhanced solutions.
Unlimited recognition such as cursive script and continuous speech/image recognition is still a
distant dream for scientists and a lot of work needs to be done to achieve that dream.
8. MuhammadGulraj
BS Computersystemengineering, GIKI,Pakistan
MS Computersystemengineering, UETPeshawar,Pakistan
8
References
1. Andrew Ng (2013), an online course for Machine learning, Stanford University,
Stanford, https://class.coursera.org/ml-004/class.
2. Duda and Hart, Pattern Classification (2001-2002), Wiley, New York.
3. Ying Cui and Zhong Jin, Facial feature points (2012),
http://www.jprr.org/index.php/jprr
4. Ioannis Dimou and Michalis Zervakis, On the analogy of classifier ensembles,
http://www.jprr.org/index.php/jprr
5. Nair, H., A system for pattern recognition and pattern summarization in multiband
satellite images, http://www.springer.com/computer/image+processing/journal/11493