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Real Time Application of Face Recognition Concept  [PDF]
Kandla Arora
International Journal of Soft Computing & Engineering , 2012,
Abstract: Face Recognition concept is one of the successful and important applications of image analysis. It’s a holistic approach towards the technology and have potential applications in various areas such as Biometrics, Information society, Law enforcement and Surveillance, Smart cards, Access control etc. This paper provides an overview of real time application of Face Recognition concept by generating a matlab code using image acquisition tool box. The basic approach used is Principal Component Analysis using Eigen faces, popularized by the seminal work of Turk and Pentland.
Application of Locality Preserving Projections in Face Recognition
International Journal of Advanced Computer Sciences and Applications , 2010,
Abstract: – Face recognition technology has evolved as anenchanting solution to address the contemporary needs in orderto perform identification and verification of identity claims. Byadvancing the feature extraction methods and dimensionalityreduction techniques in the application of pattern recognition, anumber of face recognition systems has been developed withdistinct degrees of success. Locality preserving projection (LPP)is a recently proposed method for unsupervised lineardimensionality reduction. LPP preserve the local structure offace image space which is usually more significant than theglobal structure preserved by principal component analysis(PCA) and linear discriminant analysis (LDA). This paperfocuses on a systematic analysis of locality-preservingprojections and the application of LPP in combination with anexisting technique This combined approach of LPP throughMPCA can preserve the global and the local structure of the faceimage which is proved very effective. Proposed approach istested using the AT & T face database. Experimental resultsshow the significant improvements in the face recognitionperformance in comparison with some previous methods.
Evolutionary Discriminant Feature Extraction with Application to Face Recognition  [cached]
Qijun Zhao,David Zhang,Lei Zhang,Hongtao Lu
EURASIP Journal on Advances in Signal Processing , 2009, DOI: 10.1155/2009/465193
Abstract: Evolutionary computation algorithms have recently been explored to extract features and applied to face recognition. However these methods have high space complexity and thus are not efficient or even impossible to be directly applied to real world applications such as face recognition where the data have very high dimensionality or very large scale. In this paper, we propose a new evolutionary approach to extracting discriminant features with low space complexity and high search efficiency. The proposed approach is further improved by using the bagging technique. Compared with the conventional subspace analysis methods such as PCA and LDA, the proposed methods can automatically select the dimensionality of feature space from the classification viewpoint. We have evaluated the proposed methods in comparison with some state-of-the-art methods using the ORL and AR face databases. The experimental results demonstrated that the proposed approach can successfully reduce the space complexity and enhance the recognition performance. In addition, the proposed approach provides an effective way to investigate the discriminative power of different feature subspaces.
Matching Edges in Images ; Application to Face Recognition  [PDF]
Joel Le Roux,Philippe Chaurand,Mickael Urrutia
Computer Science , 2006,
Abstract: This communication describes a representation of images as a set of edges characterized by their position and orientation. This representation allows the comparison of two images and the computation of their similarity. The first step in this computation of similarity is the seach of a geometrical basis of the two dimensional space where the two images are represented simultaneously after transformation of one of them. Presently, this simultaneous representation takes into account a shift and a scaling ; it may be extended to rotations or other global geometrical transformations. An elementary probabilistic computation shows that a sufficient but not excessive number of trials (a few tens) ensures that the exhibition of this common basis is guaranteed in spite of possible errors in the detection of edges. When this first step is performed, the search of similarity between the two images reduces to counting the coincidence of edges in the two images. The approach may be applied to many problems of pattern matching ; it was checked on face recognition.
An Application of Face Recognition System using Image Processing and Neural Networks  [PDF]
Rakesh Rathi,Manish Choudhary,Bhuwan Chandra
International Journal of Computer Technology and Applications , 2012,
Abstract: In recent years face recognition has receivedsubstantial attention from both research communities and themarket, but still remained very challenging in realapplications. A lot of face recognition algorithms, along withtheir medications, have been developed during the pastdecades. A number of typical algorithms are presented.In this paper, we propose to label a Self-Organizing Map(SOM) to measure image similarity. To manage this goal, wefeed Facial images associated to the regions of interest into theneural network. At the end of the learning step, each neuralunit is tuned to a particular Facial image prototype. Facialrecognition is then performed by a probabilistic decision rule.This scheme offers very promising results for faceidentification dealing with illumination variation and facialposes and expressions. This paper presents a novel Self-Organizing Map (SOM) for face recognition. The SOMmethod is trained on images from one database. The novelty ofthis work comes from the integration of A facial recognitionsystem is a computer application for automatically identifyingor verifying a person from a digital image or a video framefrom a video source. One of the way is to do this is bycomparing selected facial features from the image and a facialdatabase. It is typically used in security systems and can becompared to other biometrics such as fingerprint or eye irisrecognition systems.
Application of DNA algorithm to face recognition

SHEN Kai,TANG Pu-ying,

计算机应用 , 2008,
Abstract: This paper proposed a new method of face recognition, which used DNA algorithm mixed with Singular Value Decomposition (SVD). It aimed to quickly reduce the recognition targets of large scale face database, and make the next recognition process use regular methods possible. The experiment was carried out on standard ORL face database. The result indicates this method avails and DNA algorithm realizes its application on face recognition.
Deep Secure Encoding: An Application to Face Recognition  [PDF]
Rohit Pandey,Yingbo Zhou,Venu Govindaraju
Computer Science , 2015,
Abstract: In this paper we present Deep Secure Encoding: a framework for secure classification using deep neural networks, and apply it to the task of biometric template protection for faces. Using deep convolutional neural networks (CNNs), we learn a robust mapping of face classes to high entropy secure codes. These secure codes are then hashed using standard hash functions like SHA-256 to generate secure face templates. The efficacy of the approach is shown on two face databases, namely, CMU-PIE and Extended Yale B, where we achieve state of the art matching performance, along with cancelability and high security with no unrealistic assumptions. Furthermore, the scheme can work in both identification and verification modes.
Daubechies Wavelet Tool: Application For Human Face Recognition
Ms. Swapna M. Patil,,Prof. Sanket B. Kasturiwala,,Prof. Sanjay O. Dahad,,Mr. C. D. Jadhav
International Journal of Engineering Science and Technology , 2011,
Abstract: In this paper fusion of visual and thermal images in wavelet transformed domain has been presented. Here, Daubechies wavelet transform, called as D2, coefficients from visual and corresponding coefficients computed in the same manner from thermal images are combined to get fused coefficients. After decomposition up to fifth level (Level 5) fusion of coefficients is done. Inverse Daubechies wavelet transform of those coefficients gives us fused face images. The main advantage of using wavelet transform is that it is well-suited to manage different image resolution and allows the image decomposition in different kinds of coefficients, while preserving the image information. Fused images thus found are passed through Principal Component Analysis (PCA) for reduction of dimensions and then those reduced fused images are classified using a multi-layer perceptron. For experiments IRIS Thermal/Visual Face Database was used. Experimental results show that the performance of the approach presented here achieves maximum success rate of 100% in many cases.
Application of Non-negative sparse matrix factorization in occluded face recognition  [cached]
LiYing Lang,XueKe Jing
Journal of Computers , 2011, DOI: 10.4304/jcp.6.12.2675-2679
Abstract: In order to reduce the impact of block for the rate of face recognition ,in this paper, through the control of sparseness in the non-negative matrix factorization , the face image do non-negative sparse coding to obtain the eigenspace for the image. The experiment uses the ORL face database. The experimental results show that using NMFs obtains Eigenfaces with the local features of face and has a strong ability to express the occluded human face. The algorithm has good adaptability to partial occlusion, and has better robustness than PCA algorithm
An adaptive K-Means Clustering Algorithm and its Application to Face Recognition
K. Rajalakshmi,B. Thilaka,N. Rajeswari
Journal of Applied Computer Science & Mathematics , 2010,
Abstract: Pattern recognition is an emerging research area that studies the operation and design of systems that recognize patterns in data. Clustering is an essential and very frequently performed task in pattern recognition and data mining.Clustering refers to the process of grouping samples so that the samples are similar within each group. The groups are called clusters. k-means is one of the simplest unsupervised learning algorithms that solve the well known clustering problem. The procedure follows a simple and easy way to classify a given dataset of n points i x through a certain number of clusters fixed apriori. The difficulty in implementing k-means method for a large database is in determining the number of clusters which has to be randomly chosen. To overcome this difficulty, we propose a variation of the k-means algorithm, where the number of clusters ‘k’ can change dynamically depending on the data points and a threshold value given as an input. The proposed algorithm is applied in face recognition which is a very complex form of pattern recognition .It is used to verify whether a test face belongs to the database of faces and if so, identifies it.
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