List of Articles Face Recognition

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  1 - Fast Automatic Face Recognition from Single Image per Person Using GAW-KNN
  Hassan Farsi Mohammad Hasheminejad
  10.7508/jist.2014.03.008
  Real time face recognition systems have several limitations such as collecting features. One training sample per target means less feature extraction techniques are available to use. To obtain an acceptable accuracy, most of face recognition algorithms need more than on More

  Real time face recognition systems have several limitations such as collecting features. One training sample per target means less feature extraction techniques are available to use. To obtain an acceptable accuracy, most of face recognition algorithms need more than one training sample per target. In these applications, accuracy of recognition dramatically reduces for the case of one training sample per target face image because of head rotation and variation in illumination state. In this paper, a new hybrid face recognition method by using single image per person is proposed, which is robust against illumination variations. To achieve robustness against head variations, a rotation detection and compensation stage is added. This method is called Weighted Graphs and PCA (WGPCA). It uses harmony of face components to extract and normalize features, and genetic algorithm with a training set is used to learn the most useful features and real-valued weights associated to individual attributes in the features. The k-nearest neighbor algorithm is applied to classify new faces based on their weighted features from the templates of the training set. Each template contains the corrected distances (Graphs) of different points on the face components and the results of Principal Component Analysis (PCA) applied to the output of face detection rectangle. The proposed hybrid algorithm is trained using MATLAB software to determine best features and their associated weights and is then implemented by using delphi XE2 programming environment to recognize faces in real time. The main advantage of this algorithm is the capability of recognizing the face by only one picture in real time. The obtained results of the proposed technique on FERET database show that the accuracy and effectiveness of the proposed algorithm. Manuscript profile
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  2 - Investigation of face recognition methods based on deep learning algorithms
  Pezhman Gholamnezhad Ehsan Sharifi
  10.61186/jstpi.21039.19.73.16
  Today, with the growth of information technology, face recognition is a challenging issue in the image and vision analysis of computers, and for this reason, in many years, many attention has been considered for many applications in different domains. There are many met More

  Today, with the growth of information technology, face recognition is a challenging issue in the image and vision analysis of computers, and for this reason, in many years, many attention has been considered for many applications in different domains. There are many methods for implementing this technology, but the general method based on the comparison of certain characteristics of the faces of individuals with a database or pre-stored information set (which can be sampled from the sampling Be the faces of people). Biometric-based technologies have been recognized in recent years as the most promising option for identifying individuals. Different methods are used in order to implement facial diagnosis. In this paper, a review of some of the well-known image processing methods is performed and the advantages and disadvantages of the designs listed in it have been investigated. Also, the implementation of facial diagnostic systems is introduced. Then, face diagnostic algorithms are categorized and introduced based on biometric characteristics. In addition, while introducing hierarchical and X model algorithms, binary and x hierarchical model, the concept of deep face recognition structure has been addressed and some of the latest algorithms produced for this purpose. In the end, some of the most important applications of facial diagnostic systems have been studied. The purpose of this article is to introduce and express deep learning algorithms in face recognition and expression of existing challenges. Manuscript profile
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  3 - A Novel Proposed Algorithm to Tackle Glasses Wearing and Beard Issues in Facial IR Recognition
  H. Komari Alaie M. Khademi
  Face recognition via thermal infrared images is a modern recognition method. It has been so interesting for many researchers during last ten years. This method which operates via thermal features and the situation of human face vessels has much more benefits than visual More

  Face recognition via thermal infrared images is a modern recognition method. It has been so interesting for many researchers during last ten years. This method which operates via thermal features and the situation of human face vessels has much more benefits than visual-based methods. In these images, the effect of environmental lights changes, which is one of the most important obstacles of face recognition via visual images, is totally eliminated. The most important face recognition problem via thermal infrared images is the existence of diffusion obstacles like glasses and beard, which block the exact extraction of the situation of face vessels. Considering the suggested algorithm, these problems have been completely solved. In this paper face recognition is done through face vessels. For extraction of the face features, the situation of vessel branches is used. Also by choosing appropriate classification, fake vessels and false branches has been omitted. On the other hand, the best feature is extracted by using Dynamic Time Wrapping algorithm which is resistant to nonlinear changes. The simulation on UTK-IRIS gallery set has showed the accurate recognition rate 95% on the images with glasses and 88% on the images with beard, so the proposed method has improved the recognition rate about 10% and 44% respectively on same gallery set compared with the best other works. Manuscript profile
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  4 - Improving Pose Manifold and Virtual Images Using Bidirectional Neural Networks in Face Recognition Using Single Image per Person
  F. Abdolali S. A. Seyed Salehi
  In this article, for the purpose of improving neural network models applied in face recognition using single image per person, a bidirectional neural network inspired of neocortex functional model is presented. In the proposed model, recognition is not performed in a si More

  In this article, for the purpose of improving neural network models applied in face recognition using single image per person, a bidirectional neural network inspired of neocortex functional model is presented. In the proposed model, recognition is not performed in a single stage, but via two bottom-up and top-down phases and the recognition results of first stage is used for model adaptation. We have applied this novel adapting model in combination with clustering person and pose information technique to separate person and pose information and to estimate corresponding manifolds. To increase the number of training samples in the classifier neural network, virtual views of frontal images in the test dataset are synthesized using estimated manifolds. Training classifier network via virtual images obtained from bidirectional network, gives an accuracy rate of 85.45% on the test dataset which shows 1.82% improvement in accuracy of face recognition compared to training classifier with virtual images obtained from clustering person and pose information network. Manuscript profile
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  5 - Providing a Face Recognition System with an Optimal Selection of Features Based on the Cuckoo Optimization Algorithm
  Farnaz Hoseini Hamed Sepehrzadeh
  Face recognition is a pattern recognition process that is specifically performed on faces. Face recognition has many applications in identifying credit cards, security systems, and other cases. Creating a face recognition system with high accuracy is a big challenge tha More

  Face recognition is a pattern recognition process that is specifically performed on faces. Face recognition has many applications in identifying credit cards, security systems, and other cases. Creating a face recognition system with high accuracy is a big challenge that has been the focus of various researchers in recent years. The feature extraction process and classification are two important issues in diagnosis systems that can play a significant role in increasing the accuracy of diagnosis. Considering this issue, in this study, taking into account the combined features and optimizing the cuckoo algorithm, a method to improve the accuracy of face recognition is proposed. In the presented method, seven features are extracted from the images in the database, and then by obtaining the feature vector, the steps related to feature selection are performed using the cuckoo algorithm. The proposed method has been implemented with MATLAB software and compared with other methods. The evaluation results show that the proposed method was able to perform the detection on the images of ORL and FDBB databases with 93.00% and 95.12% accuracy, respectively. The result obtained for this evaluation criterion has a higher value than other compared methods. Manuscript profile
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  6 - Face recognition and Liveness Detection Based on Speech Recognition for Electronical Authentication
  Ahmad dolatkhah Behnam Dorostkar Yaghouti raheb hashempour
  As technology develops, institutions and organizations provide many services electronically and intelligently over the Internet. The police, as an institution that provides services to people and other institutions, aims to make its services smarter. Various electronic More

  As technology develops, institutions and organizations provide many services electronically and intelligently over the Internet. The police, as an institution that provides services to people and other institutions, aims to make its services smarter. Various electronic and intelligent systems have been offered in this regard. Because these systems lack authentication, many services that can be provided online require a visit to +10 police stations. Budget and equipment limitations for face-to-face responses, limitations of the police force and their focus on essential issues, a lack of service offices in villages and a limited number of service offices in cities, and the growing demand for online services, especially in crisis situations like Corona disease, electronic authentication is becoming increasingly important. This article reviews electronic authentication and its necessity, liveness detection methods and face recognition which are two of the most important technologies in this area. In the following, we present an efficient method of face recognition using deep learning models for face matching, as well as an interactive liveness detection method based on Persian speech recognition. A final section of the paper presents the results of testing these models on relevant data from this field. Manuscript profile