high speed face recognition


A Matlab Based High Speed Face Recognition System Using SOM Neural Networks
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Face recognition (FR) is a challenging issue due to variations in pose, illumination, and expression. The search results for most of the existing FR methods are satisfactory but still included irrelevant images for the target image. We have introduced a new technique uses

Pattern Recognition Of Simple Shapes In A Matlab/Simulink Environment: Design And Development Of An Efficient High – Speed Face Recognition System
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Automatic recognition of people is a challenging problem which has received much attention during the recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a

Deep face recognition .
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The goal of this paper is face recognition from either a single photograph or from a set of faces tracked in a video. Recent progress in this area has been due to two factors:(i) end to end learning for the task using a convolutional neural network (CNN), and (ii) the availability

A direct LDA algorithm for high-dimensional data with application to face recognition
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Linear discriminant analysis (LDA) has been successfully used as a dimensionality reduction technique to many classi cation problems, such as speech recognition , face recognition , and multimedia information retrieval. The objective is to nd a projection A that

Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods
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Principal Component Analysis and Fisher Linear Discriminant methods have demonstrated their success in face detection, recognition and tr acking. The representations in these subspace methods are based on second order statistics of the image set, and do not

Face recognition in human extrastriate cortex
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I. Twenty-four patients with electrodes chronically implanted on the surface of extrastriate visual cortex viewed faces, equiluminant scrambled faces, cars, scrambled cars, and butterflies. 2. A surface-negative potential, N200, was evoked by faces but not by the other

Face recognition using temporal image sequence
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We present a face recognition method using image sequence. As input we utilize plural face images rather than asingle-shot, so that the input re ects variation of facial expression and face direction. For the identi cation of the face , we essentially form a subspace with the

Face recognition using the nearest feature line method
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In this paper, we propose a novel classification method, called the nearest feature line (NFL), for face recognition . Any two feature points of the same class (person) are generalized by the feature line (FL) passing through the two points. The derived FL can

Face recognition using kernel direct discriminant analysis algorithms
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Techniques that can introduce low-dimensional feature representation with enhanced discriminatory power is of paramount importance in face recognition (FR) systems. It is well known that the distribution of face images, under a perceivable variation in viewpoint

Robust sparse coding for face recognition
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Recently the sparse representation (or coding) based classification (SRC) has been successfully used in face recognition . In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation fidelity is measured by the

Face recognition /detection by probabilistic decision-based neural network
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This paper proposes a face recognition system based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming

Face recognition using line edge map
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ABSTRACT The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of

Support vector machines applied to face recognition
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Face recognition is a K class problem. where K is the number of known individuals; and support vector machines (SVMs) are a binary classification method. By reformulating the face recognition problem and reinterpreting the output of the SVM classifier. we developed a ABSTRACT A feature-based approach to face recognition in which the features are derived from the intensity data without assuming any knowledge of the face structure is presented. The feature extraction model is biologically motivated, and the locations of the features often

A survey of face recognition techniques.
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Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be

Face recognition by elastic bunch graph matching
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We present a system for recognizing human faces from single images out of a large database with one image per person. The task is di cult because of image variance in terms of position, size, expression and pose. The system collapses most of this variance by

EMPATH: Face , emotion, and gender recognition using holons
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female subjects IS reduced rom network The extracted features do not correspond to in previ~ us face recognition systems (Ka R na~ e, 19~;) y . .. dtances between facial elements. at. er,..\f~ tures we call

Beyond eigenfaces: Probabilistic matching for face recognition
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We propose a technique for direct visual matching for face recognition and database search, using a probabilistic measure of similarity which is based on a Bayesian analysis of image di erences. Specically we model two mutually exclusive classes of variation between facial Face recognition from a representation based on features extracted from range images is explored. Depth and curvature features have several advantages over more traditional intensity-based features. Specifically, curvature descriptors have the potential for higher

Mothers face recognition by neonates: A replication and an extension
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France experimenters have that 4-day-old look longer their mothers than at strangers face . have rephcated finding under where the are only with visual on identity, all the stimuli

Face recognition using hidden Markov models
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This dissertation introduces work on face recognition using a novel technique based on Hidden Markov Models (HMMs). Through the integration of a priori structural knowl-edge with statistical information, HMMs can be used successfully to encode face features. The



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