EVALUATION OF FACE RECOGNITION METHODS

Face recognition is an example of advanced object recognition. The process is influenced by several factors such as shape, reflectance, pose, occlusion and illumination which make it even more difficult. Today there exist many well known techniques to try to recognize a face. We present to the reader an investigation into individual strengths and weaknesses of the most common techniques including feature based methods, PCA based eigenfaces, LDA based fisherfaces, ICA, Gabor wavelet based methods, neural networks and hidden Markov models. Hybrid systems try to combine the strengths and suppress the weaknesses of the different techniques either in a parallel or serial manner. Today there exist many well known techniques to try to recognize a face. Experiments done with implementations of different methods have shown that they have individual strengths and weaknesses. Hybrid systems try to combine the strengths and suppress the weaknesses of the different techniques either in a parallel or serial manner. The paper is to evaluate the different techniques and consider different combinations of these. Here we compare or evaluate templates based and geometry based face recognition, also give the comprehensive survey based face recognition methods.

[1]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[2]  Shigeyuki Tomita,et al.  Face identification using thermal image processing , 1997, Proceedings 6th IEEE International Workshop on Robot and Human Communication. RO-MAN'97 SENDAI.

[3]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[4]  Javier Ruiz-del-Solar,et al.  Analysis and Comparison of Eigenspace-Based Face Recognition Approaches , 2002, Int. J. Pattern Recognit. Artif. Intell..

[5]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[6]  Hakan Cevikalp,et al.  Discriminative common vectors for face recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[8]  Ah Chung Tsoi,et al.  Face recognition: a convolutional neural-network approach , 1997, IEEE Trans. Neural Networks.

[9]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Hisham Othman,et al.  Low complexity 2-D Hidden Markov Model for face recognition , 2000, 2000 IEEE International Symposium on Circuits and Systems. Emerging Technologies for the 21st Century. Proceedings (IEEE Cat No.00CH36353).

[11]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[12]  Hiroshi Murase,et al.  Subspace methods for robot vision , 1996, IEEE Trans. Robotics Autom..

[13]  Carlos E. Thomaz,et al.  Design of radial basis function network as classifier in face recognition using eigenfaces , 1998, Proceedings 5th Brazilian Symposium on Neural Networks (Cat. No.98EX209).

[14]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[15]  E. Walia.  Face Recognition Using Improved Fast PCA Algorithm , 2008, 2008 Congress on Image and Signal Processing.

[16]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[17]  Marc Parizeau,et al.  Experiments on eigenfaces robustness , 2002, Object recognition supported by user interaction for service robots.

[18]  Laurenz Wiskott,et al.  Face recognition by dynamic link matching , 1996 .

[19]  Lawrence Sirovich,et al.  The global dimensionality of face space , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[20]  Wenyi Zhao,et al.  Discriminant component analysis for face recognition , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[21]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[22]  Monson H. Hayes,et al.  A hidden markov model-based approach for face detection and recognition , 1999 .

[23]  Roberto Brunelli,et al.  Face Recognition: Features Versus Templates , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  L Sirovich,et al.  Low-dimensional procedure for the characterization of human faces. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[25]  Hiroshi Murase,et al.  Visual learning and recognition of 3-d objects from appearance , 2005, International Journal of Computer Vision.

[26]  Marian Stewart Bartlett,et al.  Independent components of face images : A representation for face recognition , 1997 .

[27]  Richard J. Mammone,et al.  Artificial neural networks for speech and vision , 1994 .

[28]  Aapo Hyvärinen,et al.  Independent Component Analysis: A Tutorial , 1999 .

[29]  Alice J. O'Toole,et al.  Connectionist models of face processing: A survey , 1994, Pattern Recognit..

[30]  Yiu-ming Cheung,et al.  An experimental study: on reducing RBF input dimension by ICA and PCA , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[31]  Raul Queiroz Feitosa,et al.  Comparing the Performance of the Discriminant Analysis and RBF Neural Network for Face Recognition , 1999 .

[32]  B. S. Manjunath,et al.  An Eigenspace Update Algorithm for Image Analysis , 1997, CVGIP Graph. Model. Image Process..

[33]  Jing-Yu Yang,et al.  Algebraic feature extraction for image recognition based on an optimal discriminant criterion , 1993, Pattern Recognit..

[34]  Hilary Buxton,et al.  Invariance in radial basis function neural networks in human face classification , 1995, Neural Processing Letters.

[35]  James V. Stone.  Independent Component Analysis: A Tutorial Introduction , 2007 .

[36]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[37]  Chengjun Liu,et al.  A Gabor feature classifier for face recognition , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[38]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[39]  Oscar Déniz-Suárez,et al.  Face recognition using independent component analysis and support vector machines , 2001, Pattern Recognit. Lett..

[40]  Jian Yang,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[41]  Agostino Poggi,et al.  A modified modular eigenspace approach to face recognition , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[42]  Robert J. Baron,et al.  Mechanisms of Human Facial Recognition , 1981, Int. J. Man Mach. Stud..

[43]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[44]  Erik Hjelmås.  Biometric Systems: A Face Recognition Approach , 2000 .

[45]  Monson H. Hayes,et al.  Hidden Markov models for face recognition , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[46]  Young-Seuk Park,et al.  Self-Organizing Map , 2008 .

[47]  Hilary Buxton,et al.  Face Recognition using Radial Basis Function Neural Networks , 1996, BMVC.

[48]  Bruce A. Draper,et al.  PCA vs. ICA: A Comparison on the FERET Data Set , 2002, JCIS.

[49]  Ren-jay Jeffrey Huang,et al.  Detection Strategies for Face Recognition Using Learning and Evolution , 1998 .

[50]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[51]  Erik Hjelmås.  Feature-Based Face Recognition , 2000 .

[52]  Bruce A. Draper,et al.  Analyzing PCA-based Face Recognition Algorithms: Eigenvector Selection and Distance Measures , 2003 .

[53]  Gerhard Rigoll,et al.  High performance face recognition using pseudo 2-D hidden Markov models , 1999, 1999 European Control Conference (ECC).

[54]  Johan Stephen Simeon Ballot.  Face recognition using Hidden Markov Models , 2005 .

[55]  Marian Stewart Bartlett,et al.  Viewpoint Invariant Face Recognition using Independent Component Analysis and Attractor Networks , 1996, NIPS.

[56]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[57]  Meng Joo Er,et al.  Face recognition using radial basis function (RBF) neural networks , 1999, Proceedings of the 38th IEEE Conference on Decision and Control (Cat. No.99CH36304).

[58]  Meng Joo Er,et al.  Face recognition with radial basis function (RBF) neural networks , 2002, IEEE Trans. Neural Networks.

[59]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[60]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[61]  Gerhard Rigoll,et al.  Improved Face Recognition Using Pseudo 2-D Hidden Markov Models , 1998 .

[62]  Ming Li,et al.  2D-LDA: A statistical linear discriminant analysis for image matrix , 2005, Pattern Recognit. Lett..

[63]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[64]  Monson H. Hayes,et al.  Face Recognition Using An Embedded HMM , 1999 .

[65]  Guo Jun.  Solution of small sample size problem in face recognition using Gabor wavelet transform , 2007 .

[66]  Prashant Parikh.  A Theory of Communication , 2010 .

[67]  Marian Stewart Bartlett,et al.  Independent component representations for face recognition , 1998, Electronic Imaging.

[68]  Ying Dai,et al.  Recognition of facial images with low resolution using a hopfield memory model , 1998, Pattern Recognit..

[69]  Chein-I Chang,et al.  Variants of Principal Components Analysis , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[70]  Teuvo Kohonen,et al.  Self-Organization and Associative Memory , 1988 .

[71]  Monson H. Hayes,et al.  Maximum likelihood training of the embedded HMM for face detection and recognition , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[72]  Sun-Yuan Kung,et al.  Face recognition/detection by probabilistic decision-based neural network , 1997, IEEE Trans. Neural Networks.

[73]  Stefan Eickeler.  Face database retrieval using pseudo 2D hidden Markov models , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[74]  Harry Wechsler,et al.  Mixture of experts for classification of gender, ethnic origin, and pose of human faces , 2000, IEEE Trans. Neural Networks Learn. Syst..

[75]  Tsuhan Chen,et al.  Pose invariant face recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[76]  Hui Kong,et al.  Two-dimensional Fisher discriminant analysis: forget about small sample size problem [face recognition applications] , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[77]  T. J. Stonham,et al.  Practical Face Recognition and Verification with Wisard , 1986 .

[78]  Michel Verleysen,et al.  Nonlinear Dimensionality Reduction , 2021, Computer Vision.

[79]  Monson H. Hayes,et al.  Face detection and recognition using hidden Markov models , 1998, Proceedings 1998 International Conference on Image Processing. ICIP98 (Cat. No.98CB36269).

[80]  Jang Kim Fung.  Face Recognition by Eigenface and Elastic Bunch Graph Matching Master of Philosophy Research Project First-term Report Supervised By , 2001 .

[81]  Harry Wechsler,et al.  Face recognition using hybrid classifier systems , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[82]  Joachim M. Buhmann,et al.  Distortion Invariant Object Recognition in the Dynamic Link Architecture , 1993, IEEE Trans. Computers.

[83]  Lawrence Sirovich,et al.  Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[84]  A. Yuille.  Deformable Templates for Face Recognition , 1991, Journal of Cognitive Neuroscience.

[85]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[86]  Rama Chellappa,et al.  Human and machine recognition of faces: a survey , 1995, Proc. IEEE.

[87]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[88]  Rama Chellappa,et al.  A feature based approach to face recognition , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[89]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[90]  Ming-Hsuan Yang,et al.  Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[91]  Bruce A. Draper,et al.  ANALYSIS OF PCA-BASED AND FISHER DISCRIMINANT-BASED IMAGE RECOGNITION ALGORITHMS , 2000 .

[92]  M. Omair Ahmad,et al.  Two-dimensional FLD for face recognition , 2005, Pattern Recognit..

[93]  A. Jonathan Howell.  Face unit radial basis function networks , 2000 .

[94]  Monson H. Hayes,et al.  An embedded HMM-based approach for face detection and recognition , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[95]  Hilary Buxton,et al.  Towards unconstrained face recognition from image sequences , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[96]  Ah Chung Tsoi,et al.  Face Recognition: A Hybrid Neural Network Approach , 1998 .

[97]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[98]  Modesto Castrillón,et al.  Face recognition using independent component analysis and support vector machines , 2003 .

[99]  Valery Starovoitov,et al.  ACCESS CONTROL BY FACE RECOGNITION USING NEURAL NETWORKS AND NEGATIVE EXAMPLES , 2002 .

[100]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[101]  Hisham Othman,et al.  A simplified second-order HMM with application to face recognition , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).

[102]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[103]  Penio S. Penev,et al.  Local feature analysis: A general statistical theory for object representation , 1996 .

[104]  Timothy F. Cootes,et al.  Automatic Interpretation and Coding of Face Images Using Flexible Models , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[105]  S. Loncaric,et al.  Face recognition from profiles using morphological operations , 1999, Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems. In Conjunction with ICCV'99 (Cat. No.PR00378).

[106]  Bruce A. Draper,et al.  A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[107]  R. Mersereau,et al.  Multilayer Perceptrons versus Hidden Markov Models: Comparisons and Applications to Image Analysis and Visual Pattern Recognition a Qualifying Examination Report , 1995 .