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Even though face recognition in frontal view and normal lighting condition works very well, the performance degenerates sharply in extreme conditions. In real applications, both the lighting and pose variation will always be encountered at the same time. Accordingly we propose an end-to-end face recognition method to deal with pose and illumination simultaneously based on convolutional neural networks...
When analysing human activities using data mining or machine learning techniques, it can be useful to infer properties such as the gender or age of the people involved. This paper focuses on the sub-problem of gender recognition, which has been studied extensively in the literature, with two main problems remaining unsolved: how to improve the accuracy on real-world face images, and how to generalise...
Face detection and face attribute recognition, as hot topics in the field of computer vision, have been well studied. However, over the years, face detection and attribute recognition are regarded as different tasks and designed separately, which ignores the fact that they both classify samples based on the knowledge of skin color, face outline and face components etc. In this paper, we describes...
In this paper, a novel deep neural network (DNN)-driven feature learning method is proposed and applied to multi-view facial expression recognition (FER). In this method, scale invariant feature transform (SIFT) features corresponding to a set of landmark points are first extracted from each facial image. Then, a feature matrix consisting of the extracted SIFT feature vectors is used as input data...
This paper presents a face recognition system developed in Android for robust mobile phone face unlock verification of several users. For the classification task standard neural networks structures in Java are used as a classifier by implementing the classical Feed-forward Neural Network (NN) with back propagation algorithm.
Face recognition under surveillance circumstances still poses a significant problem due to low data quality. Nevertheless, automatic analysis is highly desired for criminal investigations due to the growing amount of security cameras worldwide. We suggest a face recognition system addressing the typical issues such as motion blur, noise or compression artifacts to improve low-quality recognition rates...
Visual attribute classification has been widely discussed due to its impact on lots of applications, such as face recognition, action recognition and scene representation. Recently, Convolutional Neural Networks (CNNs) have demonstrated promising performance in image recognition, object detection and many other computer vision areas. Such networks are able to automatically learn a hierarchy of discriminate...
We present a study on the effects of focus on object instance recognition (identifying instances of the same object or very similar object, for example a particular product) using Convolutional Neural Networks. The field of object detection is seen as an harder task than that of recognition, as the object must be localised as well as classified. In the field of face recognition, alignment is seen...
Owing to the vigorous development of face recognition, near-infrared (NIR) face recognition technology with light insensitivity has attracted increasing attention. However, the traditional methods for NIR face recognition feature the hand-crafted feature design. In this paper, we present a convolutional neural network (CNN) for NIR face recognition. CNN is a multiplayer feed-forward neural network...
Although there exists some methods for coin detection and recognition, it is still a challenging task, especially for coins in natural scene. This paper proposed a method to detect and recognize the coins in natural scene. In the detection part, the Hough detection method is applied to detect the coin areas in the images. Then radius ratio, color feature and relative position constraints are used...
On the basis of explaining the principles of wavelet transform, neural network, and wavelet neural network, the paper examines two methods of face recognition: one is based on neural network, the other is based on wavelet neural network. The paper also offers the features and differences based on algorithmic simulation. The result of the stimulation reveals that face recognition using wavelet neural...
Recently, deep convolutional neural networks have set a new trend in fields of face recognition by improving the state-of-the-art performance. By using deep neural networks, much more sophisticated and high level abstracted features can be learned automatically. In this paper, we propose a method for face recognition using multi-scale convolution layer blocks and triplets of faces in unconstrained...
Multi-modal speaker recognition has received a lotof attention in recent years due to the growing security demands in real applications. In this paper, we present an efficient audio-visual speaker recognition method by fusing face and audio via the multi-modal correlated neural networks. Within our proposed approach, the facial features learned by convolutional neural networks are compatible with...
A method for synthesizing visible spectrum face imagery from polarimetric-thermal face imagery is presented. This work extends recent within-spectrum (i.e., visible-to-visible) reconstruction techniques for image representation understanding using convolutional neural networks. Despite the challenging task, we effectively demonstrate the ability to produce a visible image from a probe polarimetric-thermal...
Proposed algorithm is a face recognition algorithm from video using Generalized mean Deep Learning Neural Network. Generalized mean provides fast convergence of the feature set and Deep learning neural network is enhanced using wavelet transform as it improves the classification efficiency of the neural network. The performance of the proposed algorithm is evaluated on PaSC and Youtube dataset. The...
Deep methods based on Convolutional Neural Networks serve as accurate facial points and body parts detectors. However, most methods do not provide a confidence score for the quality of the localization process. In real world applications, such a score could be invaluable. We, therefore, study the problem of estimating the success of the localization process during test time. Our method is based on...
Paper describes an investigation of simplified neocognitron neural network model as a tool for practical recognition of handwritten mark images. Simplification of neocognitron structure from only two stages and fixed number of feature-extraction planes is proposed, the overall stages of solving practical image processing problem are described. Recognition properties of simplified net are investigated,...
The work presents an approach towards facial emotion recognition using face dataset consisting of four classes of emotions (happy, angry, neutral and sad) with different models of deep neural networks and compares their performance. We take the raw pixels values of all images in CMU face images dataset. The pixels values were represented by higher level concepts by feeding them into Restricted Boltz-mann...
The problem of emotion state recognition using the sigma-pi artificial neural network is considered. The specific feature of the considered network is the presence of two different types of activation functions: sigmoid and bell-shaped. A learning algorithm for the sigma-pi network is proposed. This algorithm is characterized by high approximation accuracy especially for nonlinear processes in real...
In this study, we present a new approach to the problem of face classification, which relies on the linguistic description of the facial features. In this method, face descriptors are represented through the Analytic Hierarchy Process (AHP) and formalized as information granules. Moreover, neural networks are used to construct efficient classifiers. Furthermore, with usage of AHP we realize a transition...
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