Local Feature Matching For Face Recognition

doi:10.1109/CRV.2006.48

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Local Feature Matching For Face Recognition

Proceedings Article•DOI•

Local Feature Matching For Face Recognition

E.F. Ersi¹, John Zelek¹•Institutions (1)

University of Waterloo¹

07 Jun 2006-pp 4-4

TL;DR: Several experiments on the FERET set of faces show the superiority of the proposed technique over all considered state-of-the-art methods, and validate the robustness of the method against facial expression variation and illumination variation.

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Abstract: In this paper a novel technique for face recognition is proposed. Using the statistical Local Feature Analysis (LFA) method, a set of feature points is extracted for each face image at locations with highest deviations from the expectation. Each feature point is described by a sequence of local histograms captured from the Gabor responses at different frequencies and orientations around the feature point. Histogram intersection is used to compare the Gabor histogram sequences in order to find the matched feature points between two faces. Recognition is performed based on the average similarity between the best matched points, in the probe face and each of the gallery faces. Several experiments on the FERET set of faces show the superiority of the proposed technique over all considered state-of-the-art methods (Elastic Bunch Graph Matching, LDA+PCA, Bayesian Intra/extrapersonal Classifier, Boosted Haar Classifier), and validate the robustness of our method against facial expression variation and illumination variation.

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Journal Article•DOI•

Optimizing feature selection in video-based recognition using Max–Min Ant System for the online video contextual advertisement user-oriented system

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Le Nguyen Bao, Dac-Nhuong Le, Gia Nhu Nguyen, Vikrant Bhateja, Suresh Chandra Satapathy¹ - Show less +1 more•Institutions (1)

Anil Neerukonda Institute of Technology and Sciences¹

01 Jul 2017-Journal of Computational Science

TL;DR: The execution assessed of the calculation is more effective than previous approaches for Video-based recognition based on FS problem and shows that the algorithm can be easily applied without the priori information of features.

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28 citations

Cites methods from "Local Feature Matching For Face Rec..."

...Page 4 of 13 Ac ce pt ed M an us cr ip t Dac-Nhuong Le et al. / Journal of Computational Science 00 (2016) 1–12 4 Table 1: Summary of approaches and methods for FR problem Approaches and methods Year Reference PCA 1991 [6] LDA 1991, 2001 [7–9] BIC 1996 [10] Combined PCA and LDA 1997 [11] SVM 2000 [12] Iterative Dynamic Programming (DP) 2000 [13] Boosted Cascade of Simple Features (BOOST) 2001 [14] Isomap extended, KDF-Isomap 2002, 2005 [15, 16] Kernel Principal Angles (KPA) 2006 [17] Locality Preserving Projections (LPP) 2006 [18] Statistical Local Feature Analysis (LFA) 2006 [19] Discriminative Canonical Correlations (DCC) 2003, 2007 [20, 21] Locally Linear Embedding (LLE) 2008 [22] Local Graph Matching (LGM) 2007 [23] KNN model and Gaussian mixture model (GMM) 2007 [24] Hidden Markov Mode (HMM) 2008 [25] 3D model based approach 2007, 2008 [26, 27] Feature Subspace Determination 2008 [28] Learning Neighborhood Discriminative Manifolds (LNDM) 2011 [19] MFA 2012 [29] NPE 2012 [30] Multidimensional scaling (MDS) 2012 [31] Data Uncertainty in Face recognition 2014 [32] Orthogonal Locality Preserving Projections (OLPP) 2015 [33] Table 2: Summary of approaches and methods in FS problem Algorithm: Approaches and methods Year References GA- Genetic algorithm 1989, 2008 [34, 39] FOCUS - Learning with many irrelevant features 1991 [35] RELIEF 1992 [36] LVW- A probabilistic wrapper approach 1996 [37] Neural Network 1997 [38] KFD- Invariant element extraction and grouping of capacity space 2000 [39] FDR- The fractal dimension 2000 [40] EBR -...
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...8 showed that our algorithms proposed have the best performance in both Honda/UCSD and CMU-MoBo data sets than NPR [2], PCA [6], LDA [9], Isomap [15, 16], KPA [17], LPP [18], LNDM [19], MFA [19], DCC [20, 21], LLE [22], NDMP [30], MDS [31]....
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...PCA 1991 [6] LDA 1991, 2001 [7–9] BIC 1996 [10] Combined PCA and LDA 1997 [11] SVM 2000 [12] Iterative Dynamic Programming (DP) 2000 [13] Boosted Cascade of Simple Features (BOOST) 2001 [14] Isomap extended, KDF-Isomap 2002, 2005 [15, 16] Kernel Principal Angles (KPA) 2006 [17] Locality Preserving Projections (LPP) 2006 [18] Statistical Local Feature Analysis (LFA) 2006 [19] Discriminative Canonical Correlations (DCC) 2003, 2007 [20, 21] Locally Linear Embedding (LLE) 2008 [22] Local Graph Matching (LGM) 2007 [23] KNN model and Gaussian mixture model (GMM) 2007 [24] Hidden Markov Mode (HMM) 2008 [25] 3D model based approach 2007, 2008 [26, 27] Feature Subspace Determination 2008 [28] Learning Neighborhood Discriminative Manifolds (LNDM) 2011 [19] MFA 2012 [29] NPE 2012 [30] Multidimensional scaling (MDS) 2012 [31] Data Uncertainty in Face recognition 2014 [32] Orthogonal Locality Preserving Projections (OLPP) 2015 [33]...
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...The comparison in Fig.8 showed that our algorithms proposed have the best performance in both Honda/UCSD and CMU-MoBo data sets than NPR [2], PCA [6], LDA [9], Isomap [15, 16], KPA [17], LPP [18], LNDM [19], MFA [19], DCC [20, 21], LLE [22], NDMP [30], MDS [31]....
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Journal Article•DOI•

Robust Face Recognition through Local Graph Matching

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Ehsan Fazl-Ersi, John S. Zelek, John K. Tsotsos

09 Jan 2007-Journal of Multimedia

TL;DR: A novel face recognition method is proposed, in which face images are represented by a set of local labeled graphs, each containing information about the appearance and geometry of a 3-tuple of face feature points, extracted using Local Feature Analysis technique.

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Abstract: A novel face recognition method is proposed, in which face images are represented by a set of local labeled graphs, each containing information about the appearance and geometry of a 3-tuple of face feature points, extracted using Local Feature Analysis (LFA) technique. Our method automatically learns a model set and builds a graph space for each individual. A two-stage method for optimal matching between the graphs extracted from a probe image and the trained model graphs is proposed. The recognition of each probe face image is performed by assigning it to the trained individual with the maximum number of references. Our approach achieves perfect result on the ORL face set and an accuracy rate of 98.4% on the FERET face set, which shows the superiority of our method over all considered state-of-the-art methods. I

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25 citations

Cites methods from "Local Feature Matching For Face Rec..."

...The BIC algorithm [3] projects the feature vector onto extra-personal and intra-personal subspaces and computes the probability that each feature vector came from one or the other subspace....
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Journal Article•DOI•

A statistical multiresolution approach for face recognition using structural hidden Markov models

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P. Nicholl¹, Abbes Amira², Djamel Bouchaffra³, R. H. Perrott¹•Institutions (3)

Queen's University Belfast¹, Brunel University London², Grambling State University³

01 Jan 2008-EURASIP Journal on Advances in Signal Processing

TL;DR: A novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM) to solve the problem of face identification.

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Abstract: This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy.

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13 citations

Journal Article•DOI•

FPGA-based IP cores implementation for face recognition using dynamic partial reconfiguration

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Ashfaq Ahmad¹, Abbes Amira², Paul Nicholl³, Benjamin Krill²•Institutions (3)

Universiti Tun Hussein Onn Malaysia¹, Ulster University², Queen's University Belfast³

01 Sep 2013-Journal of Real-time Image Processing

TL;DR: An approach for face recognition based on coefficient selection for DWT is presented, and the significant of DWT coefficient threshold selection is also analysed.

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Abstract: This paper presents a combination of novel feature vectors construction approach for face recognition using discrete wavelet transform (DWT) and field programmable gate array (FPGA)-based intellectual property (IP) core implementation of transform block in face recognition systems. Initially, four experiments have been conducted including the DWT feature selection and filter choice, features optimisation by coefficient selections and feature threshold. To examine the most suitable method of feature extraction, different wavelet quadrant and scales have been evaluated, and it is followed with an evaluation of different wavelet filter choices and their impact on recognition accuracy. In this study, an approach for face recognition based on coefficient selection for DWT is presented, and the significant of DWT coefficient threshold selection is also analysed. For the hardware implementation, two architectures for two-dimensional (2-D) Haar wavelet transform (HWT) IP core with transpose-based computation and dynamic partial reconfiguration (DPR) have been synthesised using VHDL and implemented on Xilinx Virtex-5 FPGAs. Experimental results and comparisons between different configurations using partial and non-partial reconfiguration processes and a detailed performance analysis of the area, power consumption and maximum frequency are also discussed in this paper.

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5 citations

Journal Article•DOI•

MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System

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Le Nguyen Bao¹, Dac-Nhuong Le¹, Gia Nhu Nguyen¹, Le Van Chung¹, Nilanjan Dey² - Show less +1 more•Institutions (2)

Duy Tan University¹, Techno India College of Technology²

01 Oct 2017-Journal of Global Information Management

TL;DR: The authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition that can be easily implemented and without any priori information of features.

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Abstract: Face recognition is an importance step which can affect the performance of the system. In this paper, the authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition. The length of the culled feature vector is adopted as heuristic information for ant's pheromone in their algorithm. They selected the optimal feature subset in terms of shortest feature length and the best performance of classifier used k-nearest neighbor classifier. The experiments were analyzed on face recognition show that the authors' algorithm can be easily implemented and without any priori information of features. The evaluated performance of their algorithm is better than previous approaches for feature selection.

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5 citations

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Journal Article•DOI•

Eigenfaces for recognition

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Matthew Turk¹, Alex Pentland¹•Institutions (1)

Massachusetts Institute of Technology¹

01 Jan 1991-Journal of Cognitive Neuroscience

TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.

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Abstract: We have developed a near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals. The computational approach taken in this system is motivated by both physiology and information theory, as well as by the practical requirements of near-real-time performance and accuracy. Our approach treats the face recognition problem as an intrinsically two-dimensional (2-D) recognition problem rather than requiring recovery of three-dimensional geometry, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. The system functions by projecting face images onto a feature space that spans the significant variations among known face images. The significant features are known as "eigenfaces," because they are the eigenvectors (principal components) of the set of faces; they do not necessarily correspond to features such as eyes, ears, and noses. The projection operation characterizes an individual face by a weighted sum of the eigenface features, and so to recognize a particular face it is necessary only to compare these weights to those of known individuals. Some particular advantages of our approach are that it provides for the ability to learn and later recognize new faces in an unsupervised manner, and that it is easy to implement using a neural network architecture.

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14,562 citations

"Local Feature Matching For Face Rec..." refers methods in this paper

...PCA+LDA [3] provides a linear transformation on PCA-projected feature vectors, by maximizing the between-class variance and minimizing the within-class variance....
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...Among the major holistic approaches developed for face recognition are Principal Component Analysis (PCA), combined Principal Component Analysis and Linear Discriminant Analysis (PCA+LDA), and Bayesian Intra-personal/Extra-personal Classifier (BIC)....
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...PCA [7] computes a reduced set of orthogonal basis vectors, called eigenfaces, from the training face images....
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...In order to validate the efficiency of our face recognition technique, we compare our results in both experiments with the results of four state-of-the-art methods: Elastic Bunch Graph Matching (EBGM), LDA+PCA, Bayesian Intra-personal/Extra-personal Classifier (BIC), and Boosted Haar Classifier [12]....
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...Penev and Atick showed how to construct, from the global PCA modes, a local topographic representation of objects in terms of local features....
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Proceedings Article•DOI•

Face recognition using eigenfaces

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Matthew Turk¹, Alex Pentland¹•Institutions (1)

Massachusetts Institute of Technology¹

03 Jun 1991

TL;DR: An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described.

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Abstract: An approach to the detection and identification of human faces is presented, and a working, near-real-time face recognition system which tracks a subject's head and then recognizes the person by comparing characteristics of the face to those of known individuals is described. This approach treats face recognition as a two-dimensional recognition problem, taking advantage of the fact that faces are normally upright and thus may be described by a small set of 2-D characteristic views. Face images are projected onto a feature space ('face space') that best encodes the variation among known face images. The face space is defined by the 'eigenfaces', which are the eigenvectors of the set of faces; they do not necessarily correspond to isolated features such as eyes, ears, and noses. The framework provides the ability to learn to recognize new faces in an unsupervised manner. >

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5,489 citations

Journal Article•DOI•

The FERET evaluation methodology for face-recognition algorithms

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P.J. Phillips, Hyeonjoon Moon, Syed A. Rizvi¹, Patrick J. Rauss²•Institutions (2)

College of Staten Island¹, United States Army Research Laboratory²

01 Oct 2000-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.

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Abstract: Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.

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4,816 citations

Journal Article•DOI•

Face recognition by elastic bunch graph matching

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Laurenz Wiskott¹, Jean Marc Fellous², N. Kuiger¹, C. von der Malsburg²•Institutions (2)

Ruhr University Bochum¹, University of Southern California²

01 Jul 1997-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: A system for recognizing human faces from single images out of a large database containing one image per person, based on a Gabor wavelet transform, which is constructed from a small get of sample image graphs.

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Abstract: We present a system for recognizing human faces from single images out of a large database containing one image per person. Faces are represented by labeled graphs, based on a Gabor wavelet transform. Image graphs of new faces are extracted by an elastic graph matching process and can be compared by a simple similarity function. The system differs from the preceding one (Lades et al., 1993) in three respects. Phase information is used for accurate node positioning. Object-adapted graphs are used to handle large rotations in depth. Image graph extraction is based on a novel data structure, the bunch graph, which is constructed from a small get of sample image graphs.

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2,934 citations

Journal Article•DOI•

Human and machine recognition of faces: a survey

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R. Chellappa¹, Charles L. Wilson², Saad Ahmed Sirohey¹•Institutions (2)

University of Maryland, College Park¹, National Institute of Standards and Technology²

01 May 1995

TL;DR: A critical survey of existing literature on human and machine recognition of faces is presented, followed by a brief overview of the literature on face recognition in the psychophysics community and a detailed overview of move than 20 years of research done in the engineering community.

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Abstract: The goal of this paper is to present a critical survey of existing literature on human and machine recognition of faces. Machine recognition of faces has several applications, ranging from static matching of controlled photographs as in mug shots matching and credit card verification to surveillance video images. Such applications have different constraints in terms of complexity of processing requirements and thus present a wide range of different technical challenges. Over the last 20 years researchers in psychophysics, neural sciences and engineering, image processing analysis and computer vision have investigated a number of issues related to face recognition by humans and machines. Ongoing research activities have been given a renewed emphasis over the last five years. Existing techniques and systems have been tested on different sets of images of varying complexities. But very little synergism exists between studies in psychophysics and the engineering literature. Most importantly, there exists no evaluation or benchmarking studies using large databases with the image quality that arises in commercial and law enforcement applications In this paper, we first present different applications of face recognition in commercial and law enforcement sectors. This is followed by a brief overview of the literature on face recognition in the psychophysics community. We then present a detailed overview of move than 20 years of research done in the engineering community. Techniques for segmentation/location of the face, feature extraction and recognition are reviewed. Global transform and feature based methods using statistical, structural and neural classifiers are summarized. >

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2,727 citations