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Photorealistic frontal view synthesis from a single face image has a wide range of applications in the field of face recognition. Although data-driven deep learning methods have been proposed to address this problem by seeking solutions from ample face data, this problem is still challenging because it is intrinsically ill-posed. This paper proposes a Two-Pathway Generative Adversarial Network (TP-GAN)...
Low-resolution (LR) is a challenging problem in the real world. In order to obtain better performance for low-resolution face recognition (LRFR), this paper employs a novel approach for matching low-resolution images with high resolution (HR) images based on two-dimensional linear discriminant analysis (2D-LDA) and metric learning method. The LR and HR images are transformed into a common space via...
Deep convolutional neural networks have achieved significant improvements on face recognition task due to their ability to learn highly discriminative features from tremendous amounts of face images. Many large scale face datasets exhibit long-tail distribution where a small number of entities (persons) have large number of face images while a large number of persons only have very few face samples...
Recognising detailed facial or clothing attributes in images of people is a challenging task for computer vision, especially when the training data are both in very large scale and extremely imbalanced among different attribute classes. To address this problem, we formulate a novel scheme for batch incremental hard sample mining of minority attribute classes from imbalanced large scale training data...
Face recognition systems as a biometrie system for human identification and verification has indeed shown a lot of progress over the years, illustrated through the various applications it has been used for. These applications include Human Computer Interaction systems, law, terrorist attacks, health and multimedia indexing. Furthermore, the installation of face recognition systems in public and private...
Nowadays face recognition plays a central role in surveillance, biometrics and security. In this paper a Field-Programmable Gate Array (FPGA) based low-cost real-time architecture for face recognition is presented. The face recognition module receives the detected faces from a video stream and processes the data with the widely used Eigenfaces, also known as the Principal Component Analysis (PCA)...
This paper presents the new face verification algorithm based on deep convolutional neural network. The algorithm produces face feature vectors, distance between these vectors allows to determine whether images from the same class. Comparative experimental results are given for LFW test database and modern face recognition algorithms. ROC-curve and equal error rate are used to determine the accuracy...
Convolutional neural network has made major progress in classification problems of general object recognition. Classification of facial images is one of them. However, expression of the networks for classification depends on datasets and the network model, which is vulnerable to changes in the adaptation range. We propose the network that has the two convolutional parts of pre-trained CNN by transfer...
In the interest of recent accomplishments in the development of deep convolutional neural networks (CNNs) for face detection and recognition tasks, a new deep learning based face recognition attendance system is proposed in this paper. The entire process of developing a face recognition model is described in detail. This model is composed of several essential steps developed using today's most advanced...
This paper proposes an effective fusion scheme for extracting more discriminative information from bimodal biometrics at data, feature and decision levels. In all these three levels of fusion, information from both face andfingerprint image of a single subject are fused to effectively represent it in a more discriminative ways. For all these three approaches, a combination of wavelet and principal...
Multimodal biometrie systems seek to alleviate some of the limitations of unimodal biometrie systems by combining multiple pieces of evidence of the same person in the deeision-making process. In this paper, a novel multimodal biometric identification system is proposed based on fusing the results obtained from both the face and the left and right irises using deep learning approaches. Firstly, the...
Some of the best current face recognition approaches use feature extraction techniques based on either Principle Component Analysis (PCA), Local Binary Patterns (LBP), Autoencoder (non-linear PCA), etc. While each of these feature techniques works fairly well, we propose to combine multiple feature extractors with deep learning in a system so that the overall face recognition accuracy can be improved...
Cross-resolution face recognition tackles the problem of matching face images with different resolutions. Although state-of-the-art convolutional neural network (CNN) based methods have reported promising performances on standard face recognition problems, such models cannot sufficiently describe images with resolution different from those seen during training, and thus cannot solve the above task...
We propose a dialogic system based on a relevance feedback strategy that allows for the semiautomatic synthesis of a facial image that only exists in a user's mind. The user is presented with several facial images and judges whether each one resembles the face that he or she is imagining. Based on the feedback from the user, a set of sample facial images are used to train an Optimum-Path Forest classifying...
Facial recognition applications present a great interest in the area of computer vision, with various methods and approaches that provide impressive performance. However, not all studies investigate the possibilities of using proper feature extraction methods with efficient classifiers, for applications that facial expression is not required for detection. In this sense, we propose another facial...
The pattern recognition in the sparse representation (SR) framework has been very successful. In this model, the test sample can be represented as a sparse linear combination of training samples by solving a norm-regularized least squares problem. However, the value of regularization parameter is always indiscriminating for the whole dictionary. To enhance the group concentration of the coefficients...
The importance of face anti-spoofing algorithms in biometric authentication systems is becoming indispensable. Recently, the success of Convolution Neural Networks (CNN) in key application areas of computer vision has encouraged its use in face biometrics for face anti-spoofing and verification applications. However, small training data has restricted the use of deep CNN architectures for face anti-spoofing...
Incremental learning allows incorporating new data in a classifier model without full retraining for computational efficiency. In this paper, we present two ways of performing incremental learning on Grassmann manifolds. In a Grassmann kernel learning framework, data are embedded on subspaces and kernels are constructed to map data subspaces to a projection space for classification. As new data samples...
Recent work in the recognition of naturalistic expressions, which is also known as spontaneous facial expressions recognition, has attracted researchers' attention due to its importance in different behavioural and clinical applications. The main design challenges in the area of emotion computing for automatic recognition of spontaneous facial expression are the face pose, capture distance, illumination...
In this paper, we propose a pedestrian attribute recognition approach and a CNN-based person re-identification framework enhanced by pedestrian attributes. The knowledge of person attributes can help video surveillance tasks like person re-identification as well as person search, semantic video indexing and retrieval to overcome viewpoint changes with their robustness to the inherent visual appearance...
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