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Face Recognition based on Hybrid Classifiers with Virtual Samples  

류연식 (LG N-Sys개발)
오세영 (포항공과대학교)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.40, no.1, 2003 , pp. 19-29 More about this Journal
Abstract
This paper presents a novel hybrid classifier for face recognition with artificially generated virtual training samples. We utilize both the nearest neighbor approach in feature angle space and a connectionist model to obtain a synergy effect by combining the results of two heterogeneous classifiers. First, a classifier called the nearest feature angle (NFA), based on angular information, finds the most similar feature to the query from a given training set. Second, a classifier has been developed based on the recall of stored frontal projection of the query feature. It uses a frontal recall network (FRN) that finds the most similar frontal one among the stored frontal feature set. For FRN, we used an ensemble neural network consisting of multiple multiplayer perceptrons (MLPs), each of which is trained independently to enhance generalization capability. Further, both classifiers used the virtual training set generated adaptively, according to the spatial distribution of each person's training samples. Finally, the results of the two classifiers are combined to comprise the best matching class, and a corresponding similarit measure is used to make the final decision. The proposed classifier achieved an average classification rate of 96.33% against a large group of different test sets of images, and its average error rate is 61.5% that of the nearest feature line (NFL) method, and achieves a more robust classification performance.
Keywords
Feature Anile; Frontal Recall Network; Hybrid Classifier;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Stan Z. Li, Juwei Lu, Face Recognition Using the Nearest Feature Line Method, IEEE Transactions on Neural Networks, 10(2), pp. 439-443, 1999   DOI   ScienceOn
2 Y. Moses, Y. Adini, S. Ullman, Face recognition : The problem of compensating for changes in illumination direction, Proc. European Conf. Computer Vision, vol. A, pp. 286-296, 1994   DOI
3 A. Samal, P. A. Iyengar, Automatic recognition and analysis of human faces and facial expression: A survey, Pattern Recognition, 25, pp. 65-77, 1992   DOI   ScienceOn
4 M. A. Turk, A. P. pentland, Eigenfaces for recognition, J. Cognitive Neuroscience, 4(1), pp. 71-86, 1991
5 C. M. Bishop, Training with noise is equivalent to tikhonov regularization, Neural Computation, 7(1), pp. 108-116, 1995   DOI   ScienceOn
6 R. Brunelli, T. Poggio, Face Recognition: Features versus templates, IEEE Trans. Pattern Anal. Machine Intelligence, 15, pp. 1042-1052, 1993   DOI   ScienceOn
7 A. J. O'Toole, H. Abdi, K. A. Deffenbacher, D. Valentin, Low dimensional representation of faces in higher dimensions of the face space, J. Opt. Soc. Amer. A(10), pp. 405-410, 1993   DOI
8 Jun Zhang, Yong Yan, Martin Lades, Face Recognition: Eigenface, Elastic Matching, and Neural Nets, Proceeding of the IEEE, 85(9), pp. 1423-1435, 1997   DOI   ScienceOn
9 S. Cho, K. Cha, Evolution of neural network training set through addition of virtual samples, International Conference on Evolutionary Computation, Nagoya, Japan, pp. 685-688, 1996   DOI
10 L. Holmstrom, P. Koistinen, Using additive noise in back-propagation training, IEEE transactions on Neural Networks, 3. pp. 24-38, 1992   DOI   ScienceOn
11 Anil K Jain, Robert P. W. Duin, Jianchang Mao, Statistical Pattern Recognition: A Review, IEEE Trans. Pattern Analysis and Machine Intelligence, 22(1), pp, 4-37, 2000   DOI   ScienceOn
12 in Jang, Sungzoon Cho, Ensemble Learning Using Observational Learning Theory, International Joint Conference on Neural Network, 1999
13 L. Breirnan, Using convex pseudo-data to increase prediction accuracy, Technical report, University of Calinfornia, 1999
14 Yong Liu, Xin Yao, Simultaneous Training of Negatively Correlated Neural Networks in an Ensemble, IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 29(6), (1999)   DOI   ScienceOn
15 Y. Abu-Mostafa, Hints, Neural Computation, 7, pp. 639-671, 1995   DOI   ScienceOn
16 Roberto Battiti, Anna Maria Colla, Democracy in Neural Nets: Voting Schemes for Classification, Neural Networks, 7(4), pp. 691-707, 1994   DOI   ScienceOn
17 D. V. Sridhar, E. B. Bartlett, R C. Seagrave, An Information Theoretic Approach for Combining Neural Network Process Models, Neural Networks, 12, pp. 915-926, 1999   DOI   ScienceOn
18 Nikhil R. Pal, Vijay Kumar Eluri, Two Efficient Connectionist Schemes for Structure Preserving Dimensionality Reduction, IEEE Transactions on Neural Networks, 9(6), pp. 1142-1154, 1998   DOI   ScienceOn
19 M. Bichsel, A. P. Pentland, Human face recognition and the face image set's topology, CVGIP: Image Understanding, 59, pp. 254-261, 1994   DOI   ScienceOn
20 S. Lawrence, C. Lee Giles, A C. Tsoi, A D. Back, Face recognition: A convolutional neural network approach, IEEE trans. Neural Net· works, 8, pp. 98-113, 1997   DOI   ScienceOn
21 Laurenz Wiskott, Kean-Marc Fellows, Norbert Kruger, and Christoph von der Malsburg, Face Recognition by Elastic Bunch Graph Matching, IEEE Trans. On PAMI, 19(7), pp. 775-779   DOI   ScienceOn
22 Chengjun Liu and Harry Wechsler, Learning the Face Space-Representation and Recognition, Proc. of the 15th International Conf. on Pattern Recognition, Vol.1 pp. 249-256   DOI
23 평은섭, 김병화, 안현식, 김도현, 무게중심을 이용한 자동얼굴인식 시스템의 구현, 전자공학회 논문지, 제 33권, B편, 제 8호, pp. 114-122, 1996   과학기술학회마을
24 Baback Moghaddarn, Wasiuddin Wahid, and Alex Pentland, Beyond Eigenface: Probabilistic Matching for Face Recognition, Proc. of the 2nd International Conf. on Automatic Face and Gesture Recognition, pp. 30-35, 2000   DOI
25 Srinivas Gitta and Harry Wechsler, Modular Forensic Architecture, Face Recognition from Theory to Applications, Springer, pp. 327-347, 1997
26 S. Gutta, J. Huang, B. Takacs, and H Wechsler, Face Recognition using Ensembles of Networks, Proc. of the 13th International Conf. on Pattern Recognition, Vol. 4, 1996   DOI
27 류연식, 오세영, 고유얼굴과 신경망 융합을 이용한 정면얼굴의 Recall 및 인식, 제 12회 영상처리 및 이해에 관한 워크？ 논문지, pp. 528-533, 2000
28 Xin Yao, and Yong Liu, Making Use of Population Information in Evolutionary Artificial Neural Networks, IEEE Trans. on Systems, Man and Cybernetics-Part B: Cybernetics, Vol. 28, No.3, 1998   DOI   ScienceOn
29 이상영, 함영국, 박래홍, 뉴로퍼지 알고리듬을 이용한 얼굴인식, 전자공학회논문지, 제 32권, B편, 제 1권, pp. 50-63, 1995   과학기술학회마을
30 David E. Moriarty and Risto Miikkulainen, Hierarchical Evolution of Neural Networks, International Conference on Evolutionary Computation, pp. 428-433,1998   DOI
31 Daniel P. Huttenlocher, Ryan H. lilien, and Clark F. Olson View-Based Recognition using an Eigenspace Approximation to the Hausdorff Measure, IEEE Trans. on PAMI, Vol 21, No.9, pp. 951-955, 1999   DOI   ScienceOn