Network For Face Recognition Research Articles

Two-step Learning of Deep Convolutional Neural Network for Discriminative Face Recognition under Varying Illumination

In real-world face recognition (FR) scenario, illumination variation has been known to be a challenging problem because face appearance dramatically changes depending on the illumination conditions. In order to deal with this illumination variation effectively, an illumination-reduced feature learning method using deep convolutional neural network (DCNN) is proposed in this paper. It is motivated by the capability of deep learning that represents highly complicated nonlinear structures. Our learning method is mainly comprised of following two-steps: 1) learning illumination patterns for eliminating illumination effect and 2) learning for maximizing discriminative power of feature representation. Experimental results on CMU Multi-PIE database have demonstrated that the proposed method outperforms the previous works in terms of FR accuracy.

Performance Evaluation of an Improved Self-organizing Feature Map and Modified Counter Propagation Network in Face Recognition

Performance Evaluation of an Improved Self-organizing Feature Map and Modified Counter Propagation Network in Face Recognition

Affective Environment for Java Programming Using Facial and EEG Recognition

We have developed an affective and intelligent learning environment that helps students to improve their Java programming skills. This environment evaluates cognitive and affective aspects of students in order to define the level of difficulty of the exercises that are more suitable for the them in its current condition. The cognitive aspects are: the number of mistakes, the difficulty level of the current exercise and the time spent in the solution. The affective aspects are: the acquired emotion from a facial expression and the acquired valence from electroencephalogram signals. This environment also uses a neural network for face recognition of basic emotions, a support vector machine to define the valence of emotion and a fuzzy inference engine to evaluate the cognitive and affective aspects.

Using neural networks for face recognition in controlled environments

This paper discusses the implementation and validation of neural networks for face recognition in controlled environments, an implementation strategy that consists of 3 stages: acquisition, extraction of facial parameters and validation using neural networks is presented. This paper seeks to validate the operation of two neural networks in particular for face recognition, these are the Perceptron and ART. We seek to analyze and validate through a series of tests the possible operation of this type of neural networks in biometric identification systems and access control.

Coupled principal component analysis based face recognition in heterogeneous sensor networks

In this paper, we construct heterogeneous sensor networks (HSN) for face recognition and propose a novel approach named coupled principal component analysis (CPCA) that uses a feature-based representation for heterogeneous face images. We first employ local binary patterns (LBP) to capture the local structure of face images, and then propose CPCA to obtain the global face information. The proposed CPCA could incorporate the information between heterogeneous feature spaces, and therefore it reduces the gap between face images captured from heterogeneous sensors in HSN. Finally, the spare representation is utilized for matching heterogeneous face images. The experimental results demonstrate that the proposed approach achieves better performance than the state-of-the-art approaches.

Information-theoretic analysis of support recovery from sparsely corrupted measurements

This paper studies the recovery of the support of sparse signal that is corrupted by both dense noise and gross error. The gross error is an unknown sparse vector whose nonzero entries maybe unbounded. This setup covers a wide range of applications, such as face recognition, inpainting and sensor networks. We derive the information-theoretic lower bounds on the sampling rate required to obtain a desired error rate, which depend on the properties of both the signal and the gross error. The investigations are given in the high-dimensional setting. Some illustrations are provided to further reveal the relationship of these bounds.

Dissociable cortical pathways for qualitative and quantitative mechanisms in the face inversion effect.

Humans' ability to recognize objects is remarkably robust across a variety of views unless faces are presented upside-down. Whether this face inversion effect (FIE) results from qualitative (distinct mechanisms) or quantitative processing differences (a matter of degree within common mechanisms) between upright and inverted faces has been intensely debated. Studies have focused on preferential responses to faces in face-specific brain areas, although face recognition also involves nonpreferential responses in non-face-specific brain areas. By using dynamic causal modeling with Bayesian model selection, here we show that dissociable cortical pathways are responsible for qualitative and quantitative mechanisms in the FIE in the distributed network for face recognition. When faces were upright, the early visual cortex (VC) and occipital and fusiform face areas (OFA, FFA) suppressed couplings to the lateral occipital cortex (LO), a primary locus of object processing. In contrast, they did not inhibit the LO when faces were inverted but increased couplings to the intraparietal sulcus, which has been associated with visual working memory. Furthermore, we found that upright and inverted face processing together involved the face network consisting of the VC, OFA, FFA, and inferior frontal gyrus. Specifically, modulatory connectivity within the common pathways (VC-OFA), implicated in the parts-based processing of faces, strongly correlated with behavioral FIE performance. The orientation-dependent dynamic reorganization of effective connectivity indicates that the FIE is mediated by both qualitative and quantitative differences in upright and inverted face processing, helping to resolve a central debate over the mechanisms of the FIE.

An Enhanced Empirical Method on Choosing the Highest Principal Features and the Number of Hidden Neurons in Principal Component Analysis-Artificial Neural Network Face Recognition based System

ABSTRACT With fast evolving technology, it is necessary to design an efficient security system which can detect unauthorized access on any system. It‟s needed to implement an extremely secure, economic and perfect system for face recognition that can protect systems from unauthorized access. So, in this paper, a robust face recognition system approach is proposed for feature extraction using Principal Component Analysis, and recognition using Feed Forward Back Propagation Neural Network. The proposed approach gave better results in all aspects including recognition rate, training time, elapsed time and mean square error. The paper shows that when using 80% of the dataset for training, the proposed system can achieve up to 97.5% recognition rate if correct number of input principal features to the classifier, learning rate, momentum, and number of hidden neurons are used. This algorithm is implemented using Matlab software, and tolerable error value used is superiorly chosen as MSE= 0.0004.

Coupled Auto-Associative Neural Networks for Heterogeneous Face Recognition

Several models have been previously suggested for learning correlated representations between source and target modalities. In this paper, we propose a novel coupled autoassociative neural network for learning a target-to-source image representation for heterogenous face recognition. This coupled network is unique, because a cross-modal transformation is learned by forcing the hidden units (latent features) of two neural networks to be as similar as possible, while simultaneously preserving information from the input. The effectiveness of this model is demonstrated using multiple existing heterogeneous face recognition databases. Moreover, the empirical results show that the learned image representation—common latent features—by the coupled auto-associative produces competitive cross-modal face recognition results. These results are obtained by training a softmax classifier using only the latent features from the source domain and testing using only the latent features from the target domain.

GPU realization of a neural network face recognition algorithm

GPU realization of a neural network face recognition algorithm

Color Face Recognition Based on Quaternion Zernike Moment Invariants and Quaternion BP Neural Network

As an active research topic, many algorithms have been presented for face recognition. However, they mainly utilize the monochromatic intensity information. Among a few color face recognition methods, most of them treat the three channels separately. In this paper, a color face image is treated in a holistic manner by using the quaternion theory. We then propose a new algorithm for color face recognition, which uses the quaternion Zernike moment invariants and the quaternion BP neural network for the color face recognition. Experimental results on the Collection of Facial Images (Grimace) database, including major expression variation and considerable variation in head turn and tilt, show that the proposed method is better than the conventional ones in recognition rate.

Security System with Face Recognition, Sms Alert and Embedded Network Video Monitoring Terminal

Even though there are various security systems consuming large power are available in market nowadays, robbery rate is very high. We are proposing a novel system to prevent robbery in highly secure areas with lesser power consumption. T his system has face-recognition technology which grants access to only authorized people to enter that area. If others enter the place without access using some other means, then the system alerts the security personnel and streams the video captured by th e security camera. The facerecognition is done using PCA algorithm. The video transmitted is compressed and transmitted by ENVMT. By using this ENVMT, the video can play with lesser bandwidth consumption, latency and jitter.

Dynamic Bayesian Network for Unconstrained Face Recognition in Surveillance Camera Networks

The demand for robust face recognition in real-world surveillance cameras is increasing due to the needs of practical applications such as security and surveillance. Although face recognition has been studied extensively in the literature, achieving good performance in surveillance videos with unconstrained faces is inherently difficult. During the image acquisition process, the noncooperative subjects appear in arbitrary poses and resolutions in different lighting conditions, together with noise and blurriness of images. In addition, multiple cameras are usually distributed in a camera network and different cameras often capture a subject in different views. In this paper, we aim at tackling this unconstrained face recognition problem and utilizing multiple cameras to improve the recognition accuracy using a probabilistic approach. We propose a dynamic Bayesian network to incorporate the information from different cameras as well as the temporal clues from frames in a video sequence. The proposed method is tested on a public surveillance video dataset with a three-camera setup. We compare our method to different benchmark classifiers with various feature descriptors. The results demonstrate that by modeling the face in a dynamic manner the recognition performance in a multi-camera network is improved over the other classifiers with various feature descriptors and the recognition result is better than using any of the single camera.

Application of PCA Based Neural Network in Face Recognition

Application of PCA Based Neural Network in Face Recognition

Multicore and GPU Parallelization of Neural Networks for Face Recognition

Training of Artificial Neural Networks for large data sets is a time consuming task. Various approaches have been proposed to reduce the efforts, many of them by applying parallelization techniques. In this paper we develop and analyze two novel parallel training approaches for Backpropagation neural networks for face recognition. We focus on two specific paralleliza- tion environments, using on the one hand OpenMP on a conventional multithreaded CPU and CUDA on a GPU. Based on our findings we give guidelines for the efficient parallelization of Backpropagation neural networks on multicore and GPU architectures.Additionally, we present a traversal method finding the best combination of learning rate and momentum term by varying the number of hidden neurons supporting the parallelization efforts.

Close Range Photogrammetry and Neural Network for Facial Recognition

Recently, there has been an increasing interest in utilizing imagery in different fields such as archaeology, architecture, mechanical inspection an d biometric identifiers where face recognition considered as one of the most important physiologic al characteristics that is related to the shape and geometry of the faces and used for identification a nd verification of a person's identity. In this stu dy, close range photogrammetry with overlapping photographs were used to create a three dimensional model of human face where coordinates of selected object points were exatrcted and used to caculate five different geometric quantities that been used as biometric authentication for uniquely recognizin g humans. Then , the probabilistic neural networks, w ith their remarkable ability to derive meaning from complicated or imprecise data, utilize the extracte d geometric quantities to find patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. Quantifiable dimensions that based on geometric attributes rathe r than radiometric characteristics has been successfully extracted using close range photogrammetry. the Probabilistic Neural Network (PNN) as a kind from radial basis network group has been use d to specify a geometrics parameters for face recognition where the designed recognition method i s not effected by face gesture or color and has lower cost compared with other techniques. This met hod is reliable and flexible with respect to the le vel of detail that describe the human surface. Experime ntal results using real data proved the feasibility and the quality of the suggested approach.

Reinforced learning in decision-based neural networks for face recognition.

Reinforced learning in decision-based neural networks for face recognition.

A Comparative Analysis of Feed-Forward and Generalized Regression Neural Networks for Face Recognition Using Principal Component Analysis

In this paper we give a comparative analysis of performance of feed forward neural network and generalized regression neural network based face recognition. We use different inner epoch for different input pattern according to their difficulty of recognition. We run our system for different number of training patterns and test the system’s performance in terms of recognition rate and training time. We run our algorithm for face recognition application using Principal Component Analysis and both neural network. PCA is used for feature extraction and the neural network is used as a classifier to identify the faces. We use the ORL database for all the experiments.

Evaluation of unsupervised feature extraction neural networks for face recognition

In this paper, new appearances based on neural networks (NN) algorithms are presented for face recognition. Face recognition is subdivided into two main stages: feature extraction and classifier. The suggested NN algorithms are the unsupervised Sanger principal component neural network (Sanger PCNN) and the self-organizing feature map (SOFM), which will be applied for features extraction of the frontal view of a face image. It is of interest to compare the unsupervised network with the traditional Eigenfaces technique. This paper presents an experimental comparison of the statistical Eigenfaces method for feature extraction and the unsupervised neural networks in order to evaluate the classification accuracies as comparison criteria. The classifier is done by the multilayer perceptron (MLP) neural network. Overcoming of the problem of the finite number of training samples per person is discussed. Experimental results are implemented on the Olivetti Research Laboratory database that contains variability in expression, pose, and facial details. The results show that the proposed method SOFM/MLP neural network is more efficient and robust than the Sanger PCNN/MLP and the Eigenfaces/MLP, when used a few number of training samples per person. As a result, it would be more applicable to utilize the SOFM/MLP NN in order to accomplish a higher level of accuracy within a recognition system.

Block-based Deep Belief Networks for face recognition

This paper presents research findings on the use of Deep Belief Networks (DBNs) for face recognition. Experiments were conducted to compare the performance of a DBN trained using whole images with that of several DBN trained using image blocks. Image blocks are obtained when the face images are divided into smaller blocks. The objective of using image blocks is to improve the performance of the present DBN to visual variations. To test this hypothesis, the proposed block-based DBN was tested on different databases, which contain a variety of visual variations. Simulation results on these databases show that the proposed block-based DBN is effective against lighting variation. The proposed approach is also compared with other illumination invariant methods and was found to demonstrate higher recognition accuracies.