This document discusses building face recognition capabilities into smartphone devices. It describes a prototype called Auto Face Tagger, developed for Android phones, that can detect faces in images and automatically tag them. The system uses a client-server architecture where the phone performs initial face detection and extracts features, sending this data to a server for facial recognition matching against a database and returning tagging information to the phone. Principal component analysis and Viola-Jones algorithms are utilized for face recognition and detection tasks on the mobile device. The goal of Auto Face Tagger is to automatically tag faces in photos for improved image searching capabilities.
FUSION BASED MULTIMODAL AUTHENTICATION IN BIOMETRICS USING CONTEXT-SENSITIVE ...cscpconf
Biometrics is one of the primary key concepts of real application domains such as aadhar card, passport, pan card, etc. In such applications user can provide two to three biometrics patterns
like face, finger, palm, signature, iris data, and so on. We considered face and finger patterns
for encoding and then also for verification. Using this data we proposed a novel model for
authentication in multimodal biometrics often called Context-Sensitive Exponent Associative Memory Model (CSEAM). It provides different stages of security for biometrics patterns. In
stage 1, face and finger patterns can be fusion through Principal Component Analysis (PCA), in stage 2 by applying SVD decomposition to generate keys from the fusion data and preprocessed face pattern and then in stage 3, using CSEAM model the generated keys can be encoded. The final key will be stored in the smart cards. In CSEAM model, exponential
kronecker product plays a critical role for encoding and also for verification to verify the chosen samples from the users. This paper discusses by considering realistic biometric data in
terms of time and space
Explaining Aluminous Ascientification Of Significance Examples Of Personal St...SubmissionResearchpa
This article lists the results of an experimental test of algorithms for recognizing ear tags. Like most biometric technologies, one of the key issues is the separation of the characteristic image, which is also included in the recognition of the person on the basis of the ear studship. Because the accuracy and accuracy of the program depends on the criteria for recognizing the person on the basis of any biometric technology. Therefore, by distinguishing the problem of discrete cosine exchange, using the main component method and algorithms for separation, the characteristic sign of the ear stud was detected. These algorithms can be used to develop personal identification systems based on earphones. by Djuraeva Rano Bahrombekovna, Mukhammadiev Alisher Numonhan-ugli, Khodjaeva Mavluda Sabirovna and Jumaev Turdali Saminjonovich 2020. Explaining Aluminous Ascientification Of Significance Examples Of Personal Study On Personal Identity. International Journal on Integrated Education. 2, 1 (Mar. 2020), 48-52. DOI:https://doi.org/10.31149/ijie.v2i1.287. https://journals.researchparks.org/index.php/IJIE/article/view/287/280 https://journals.researchparks.org/index.php/IJIE/article/view/287
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.
AI Approach for Iris Biometric Recognition Using a Median FilterNIDHI SHARMA
The Artificial Intelligence approach is used for Iris recognition by understanding the distinctive and measurable characteristics of the human body such as a person’s face, iris, DNA, fingerprints, etc. AI methods analyzed the attributes like iris images. Privacy and Security being a major concern nowadays, Recognition Technique can find numerous applications.
FUSION BASED MULTIMODAL AUTHENTICATION IN BIOMETRICS USING CONTEXT-SENSITIVE ...cscpconf
Biometrics is one of the primary key concepts of real application domains such as aadhar card, passport, pan card, etc. In such applications user can provide two to three biometrics patterns
like face, finger, palm, signature, iris data, and so on. We considered face and finger patterns
for encoding and then also for verification. Using this data we proposed a novel model for
authentication in multimodal biometrics often called Context-Sensitive Exponent Associative Memory Model (CSEAM). It provides different stages of security for biometrics patterns. In
stage 1, face and finger patterns can be fusion through Principal Component Analysis (PCA), in stage 2 by applying SVD decomposition to generate keys from the fusion data and preprocessed face pattern and then in stage 3, using CSEAM model the generated keys can be encoded. The final key will be stored in the smart cards. In CSEAM model, exponential
kronecker product plays a critical role for encoding and also for verification to verify the chosen samples from the users. This paper discusses by considering realistic biometric data in
terms of time and space
Explaining Aluminous Ascientification Of Significance Examples Of Personal St...SubmissionResearchpa
This article lists the results of an experimental test of algorithms for recognizing ear tags. Like most biometric technologies, one of the key issues is the separation of the characteristic image, which is also included in the recognition of the person on the basis of the ear studship. Because the accuracy and accuracy of the program depends on the criteria for recognizing the person on the basis of any biometric technology. Therefore, by distinguishing the problem of discrete cosine exchange, using the main component method and algorithms for separation, the characteristic sign of the ear stud was detected. These algorithms can be used to develop personal identification systems based on earphones. by Djuraeva Rano Bahrombekovna, Mukhammadiev Alisher Numonhan-ugli, Khodjaeva Mavluda Sabirovna and Jumaev Turdali Saminjonovich 2020. Explaining Aluminous Ascientification Of Significance Examples Of Personal Study On Personal Identity. International Journal on Integrated Education. 2, 1 (Mar. 2020), 48-52. DOI:https://doi.org/10.31149/ijie.v2i1.287. https://journals.researchparks.org/index.php/IJIE/article/view/287/280 https://journals.researchparks.org/index.php/IJIE/article/view/287
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.
AI Approach for Iris Biometric Recognition Using a Median FilterNIDHI SHARMA
The Artificial Intelligence approach is used for Iris recognition by understanding the distinctive and measurable characteristics of the human body such as a person’s face, iris, DNA, fingerprints, etc. AI methods analyzed the attributes like iris images. Privacy and Security being a major concern nowadays, Recognition Technique can find numerous applications.
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.
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 DETECTION AND FEATURE EXTRACTION FOR FACIAL EMOTION DETECTIONvivatechijri
: Facial emotion Recognition has been a major issue and an advanced area of research in the field of HumanMachine Interaction and Image Processing. To get facial expression the system needs to meet a variety of human facial
features such as color, body shape, reflection, posture, etc. To get a person's facial expression first it is necessary to get
various facial features such as eye movement, nose, lips, etc. and then differentiate by comparing the trained data using
differentiation appropriate for speech recognition. An AI-based approach to the novel visual system system is suggested.
There are two main processes in the proposed system, namely Face detection and feature extraction.Face detection is
performed using the Haar Cascade Method. The proper feature extraction method is used to extract the element and then
used a vector machine to distinguish the final face shape. The FER13 data set is used for training.
Review of face detection systems based artificial neural networks algorithmsijma
Face detection is one of the most relevant applications of image processing and biometric systems.
Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition.
There is lack of literature surveys which give overview about the studies and researches related to the using
of ANN in face detection. Therefore, this research includes a general review of face detection studies and
systems which based on different ANN approaches and algorithms. The strengths and limitations of these
literature studies and systems were included also.
Comparative Analysis of Face Recognition Methodologies and TechniquesFarwa Ansari
In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
approaches are basically used for face recognition
methodologies. Eigen Faces, Fisher Faces and LBP
methodologies were introduced for recognition purpose.
Eigen Faces is most frequently used because of its
efficiencies. To observe the efficient techniques of facial
recognition, there are many scenarios to measure its
performance which are based on real time.
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Review of facial expression recognition system and used datasetseSAT Journals
Abstract The human face is main part to recognize the individuals as well as provides the important information, current state of user behavior through their different expressions. Therefore, in biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. The other areas which use such technique are computer science medicine, psychology etc. Usually face recognition system is consisting of many internal tasks. Face detection is thefirst task of such systems. Due to different variations across the human faces, the process of detecting face becomes complex. But with help of different modeling methods, it becomes possible to recognize the face and hence different face expressions. This paperpresents a literature review over the techniques and methods used for facial expression recognition. Also, different facial expression datasets available for the research or testing of existing methods of facial expression recognition are discussed. Keywords: Facial Expression, Face Detection, Features Extraction, Recognition, datasets.
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.
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 DETECTION AND FEATURE EXTRACTION FOR FACIAL EMOTION DETECTIONvivatechijri
: Facial emotion Recognition has been a major issue and an advanced area of research in the field of HumanMachine Interaction and Image Processing. To get facial expression the system needs to meet a variety of human facial
features such as color, body shape, reflection, posture, etc. To get a person's facial expression first it is necessary to get
various facial features such as eye movement, nose, lips, etc. and then differentiate by comparing the trained data using
differentiation appropriate for speech recognition. An AI-based approach to the novel visual system system is suggested.
There are two main processes in the proposed system, namely Face detection and feature extraction.Face detection is
performed using the Haar Cascade Method. The proper feature extraction method is used to extract the element and then
used a vector machine to distinguish the final face shape. The FER13 data set is used for training.
Review of face detection systems based artificial neural networks algorithmsijma
Face detection is one of the most relevant applications of image processing and biometric systems.
Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition.
There is lack of literature surveys which give overview about the studies and researches related to the using
of ANN in face detection. Therefore, this research includes a general review of face detection studies and
systems which based on different ANN approaches and algorithms. The strengths and limitations of these
literature studies and systems were included also.
Comparative Analysis of Face Recognition Methodologies and TechniquesFarwa Ansari
In the field of computer sciences such as
graphics and also analyzing the image and its processing,
face recognition is the most prominent problem due to the
comprehensive variation of faces and the complexity of
noises and image backgrounds. The purpose and working
of this system is that it identifies the face of a person from
the real time video and verifies the person from the images
store in the database. This paper provides a review of the
methodologies and techniques used for face detection and
recognition. Firstly a brief introduction of Facial
Recognition is given then the review of the face
recognition’s working which has been done until now, is
briefly introduced. Then the next sections covered the
approaches, methodologies, techniques and their
comparison. Holistic, Feature based and Hybrid
approaches are basically used for face recognition
methodologies. Eigen Faces, Fisher Faces and LBP
methodologies were introduced for recognition purpose.
Eigen Faces is most frequently used because of its
efficiencies. To observe the efficient techniques of facial
recognition, there are many scenarios to measure its
performance which are based on real time.
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Review of facial expression recognition system and used datasetseSAT Journals
Abstract The human face is main part to recognize the individuals as well as provides the important information, current state of user behavior through their different expressions. Therefore, in biometric area of the research, automatically face & face expression recognition attracts researcher’s interest. The other areas which use such technique are computer science medicine, psychology etc. Usually face recognition system is consisting of many internal tasks. Face detection is thefirst task of such systems. Due to different variations across the human faces, the process of detecting face becomes complex. But with help of different modeling methods, it becomes possible to recognize the face and hence different face expressions. This paperpresents a literature review over the techniques and methods used for facial expression recognition. Also, different facial expression datasets available for the research or testing of existing methods of facial expression recognition are discussed. Keywords: Facial Expression, Face Detection, Features Extraction, Recognition, datasets.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
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.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.