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Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using...
The major limitation in current facial recognition systems is that they do not perform very well in uncontrolled environments, that is, when faces present variations in pose, illumination, facial expressions and environment. This is a serious obstacle in applications such as law enforcement and surveillance systems. To address this limitation, in this paper we introduce an improved approach to ensure...
Facial expression recognition is an active area of research with applications in the design of Human Computer Interaction (HCI) systems. In this paper, we propose an approach for facial expression recognition using deep convolutional neural networks (CNN) based on features generated from depth information only. The Gradient direction information of depth data is used to represent facial information,...
While classical face recognition (FR) technologies are mainly based on static images, video-based FR is concerned with the matching of two image sets containing facial images captured from each video. Video based FR is supposed to be advantageous as it takes more abundant information in to account to improve accuracy and robustness. Though many methods have been proposed, there still exists a variety...
We present a new technique to infer dimensions that can be used in biometric face recognition. The methodology is centered on inferring unique dimensions from human ears which provides unique physical biometric features. The process of determining the distance is done by harvesting the real actual dimensions from 2D faces images. This is achieved by using specific point to point distances on the two...
Biometric recognition became an integral part of our living. This paper deals with machine learning methods for recognition of humans based on face and iris biometrics. The main intention of machine learning area is to reach a state when machines (computers) are able to respond without humans explicitly programming them. This area is closely related to artificial intelligence, knowledge discovery,...
Image recognition is very key importance for image process. Thus, the novel image recognition method based on the hybrid model of support vector machine and artificial life is presented in the paper. The artificial life is the important research method for the traditional biology and the ecology, and the research of artificial life contributes to indicate the most essential feature that life needs...
Recently, deep neural networks have been shown to perform competitively on the task of predicting facial expression from images. Trained by gradient-based methods, these networks are amenable to "multi-task" learning via a multiple term objective. In this paper we demonstrate that learning representations to predict the position and shape of facial landmarks can improve expression recognition...
Developing a security module with accurate, effective face recognition and fingerprint recognition is dealt in my paper. Personal safety and unique identification of a individual is necessary and it plays an vital role in certain situations where only the authorized persons can access the resources particularly in the secured storage of medicines, jewels, documentations, mines, militaries, laboratories,...
Face detection is a technique of detecting any face from a set of images. Face can be detected on the basis of features of the face such as pose, height, width etc. Although there are various techniques implemented for the detection of faces such as face detection using neural networks, but the features extracted using neural network is not sufficient and has low accuracy. Hence in this paper an efficient...
We present a new approach to localize extensive facial landmarks with a coarse-to-fine convolutional network cascade. Deep convolutional neural networks (DCNN) have been successfully utilized in facial landmark localization for two-fold advantages: 1) geometric constraints among facial points are implicitly utilized, 2) huge amount of training data can be leveraged. However, in the task of extensive...
Face recognition is a topic of great interest in different areas, especially those related to security. The identification of a person by the image of her face is a difficult task because of changes experienced by the face due to various factors, such as facial expression, aging and even the lighting. This paper presents a new face recognition technique based on the combination of a competitive fuzzy...
Neural Networks have been widely used in face recognition as a reliable classifier. In the proposed method, neural network classifier with CSD coefficients is used to speed up the recognition system. The FPGA implementation of the proposed method indicates that the high speed recognition can be achieved by using neural network classifier with CSD coefficients while maintaining good recognition rate.
In this paper, a new face recognition method based on PCA (principal Component Analysis), LDA (Linear Discriminant Analysis) and neural networks is proposed. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available. The proposed method was tested on ORL face database. Experimental results on this database demonstrated the effectiveness of the...
This paper analyses the security of a recently proposed chaos based cryptosystem. It shows that the cryptosystem under study has weak diffusion and presents a cryptanalysis that allows the attacker to decrypt any encrypted image. More precisely, it proposes a divide-and-conquer attack that allows an attacker to recover the internal states of the cryptosystem and to use them in order to encrypt or...
Automatic analysis of human facial expression is one of the challenging problems in machine vision systems. The most expressive way humans display emotion is through facial expression. In this paper, we extend texture based facial expression recognition, with a method of 2D image processing implemented for extraction of features and a new neural network based decision trees. The algorithm applies...
The interest towards biometric approach to identity verification is high, because of the need to protect everything that could have a value for some purpose. Face recognition is one of these biometric techniques, having its greater advantage in requiring a limited interaction by user. We present a Face Recognition System (FRS) based on multiple neural networks using a belief revision mechanism. Each...
Over the past few decades, biometric recognition firmly established itself as one of the areas of tremendous potential to make scientific discovery and to advance state-of-the- art research in security domain. Hardly, there is a single area of IT left untouched by increased vulnerabilities, on-line scams, e-fraud, illegal activities, and event pranks in virtual worlds. In parallel with biometric development,...
This paper presents a method of recognizing faces from frontal pose images by using Circularly Orthogonal Moments (COM). In the presented method, first Pseudo Zernike Moment (PZM), Zernike Moment (ZM) and Polar Cosine Transform (PCT) were employed to extract features from the global information of images, and then Radial Basis Function (RBF) Network and Genetic Algorithm (GA) were used for face recognition...
In order to raise the efficiency of face recognition, a method based on back-propagation (BP) neural network and probabilistic neural network (PNN) integration was introduced. The method uses the kernel independent component analysis (KICA) to extract facial features, puts eigenvectors into the BP neural networks and PNN to learn, and outputs the two classification and recognition results by relative...
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