[논문]Face Recognition Based on Improved Fuzzy RBF Neural Network for Smar t Device

Lee, Eung-Joo

doi:10.9717/kmms.2013.16.11.1338

Face Recognition Based on Improved Fuzzy RBF Neural Network for Smar t Device 원문보기

멀티미디어학회논문지 = Journal of Korea Multimedia Society, v.16 no.11, 2013년, pp.1338 - 1347

Lee, Eung-Joo (Department of Information & Communications Engineering, Tongmyong University)

Abstract ▼ AI-Helper

Face recognition is a science of automatically identifying individuals based their unique facial features. In order to avoid overfitting and reduce the computational reduce the computational burden, a new face recognition algorithm using PCA-fisher linear discriminant (PCA-FLD) and fuzzy radial basis function neural network (RBFNN) is proposed in this paper. First, face features are extracted by the principal component analysis (PCA) method. Then, the extracted features are further processed by the Fisher's linear discriminant technique to acquire lower-dimensional discriminant patterns, the processed features will be considered as the input of the fuzzy RBFNN. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on Levenberg-Marquart (L-M) for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm. Experimental results on the ORL face database demonstrate that the proposed algorithm has satisfactory performance and high recognition rate.

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문제 정의

In this paper, a general design approach using an fuzzy RBF neural network classifier for face recognition to cope with small training sets of high-dimensional problem is presented. Firstly, face features are first extracted by PCA algorithm.

가설 설정

Generally speaking, multi-layer networks usually coupled with the backpropagation (BP) algorithm, are most widely used in face recognition [12]. Yet, two major criticisms are commonly raised against the BP algorithm:¹⁾ It is computationally intensive because of its slow convergence speed and²⁾ there is no guarantee at all that the absolute minima can be achieved. On the other hand, RBF neural networks have recently attracted extensive interests in the community of neural networks for a wide range of applications[13,14,22].

제안 방법

Based on the RBF neural network above, we construct the environment parameters of network to give the fuzzy inference ability to it, fuzzy characteristics improved the learning generalization ability of neural networks and made a better approximation to the actual models.
In this paper, we use a hybrid algorithm, which combine PCA algorithm with FLD algorithm, for feature extraction. The number of input variables is reduced through feature selection, i.

대상 데이터

The selected 100 images are implemented as the set of training. Meanwhile, we select the other 100 images form 20 persons for testing. The original image with size of 92*112 can be translated into a 10304*1 matrix by PCA algorithm, and we select fisrt 60 eigenfaces for experiments.

이론/모형

Then, the resulting features are further projected into the Fisher's optimal subspace in which the ratio of the between-class scatter and the within-class scatter is maximized. As a widely applied algorithm in fuzzy RBF neural network, BP learning algorithm has the low rate of convergence, therefore, an improved learning algorithm based on L-M for fuzzy RBF neural network is introduced in this paper, which combined the Gradient Descent algorithm with the Gauss-Newton algorithm, it has both local performance and entire performance.
Though we can apply the gradient paradigm to find the entire set of optimal parameters, the paradigm is generally slow and likely to become trapped in local minima. For solving this problem, Levenberg-Marquart (L-M) training algorithm was used to adjust wi in this paper. L-M algorithm is an improved training algorithm of BP, which combined the advantages of gradient paradigm and newton method, it has both local performance and entire performance.

후속연구

Meanwhile, this algorithm can also reduce the computational complexity. The feature vectors are only extracted from gray-scale images, more features extracted from both gray-scale and spatial texture information and a real-time face recognition system will be studied in the future work.

참고문헌 (22)

R. Chellappa, C.L. Wilson, and S. Sirohey, "Human and Machine Recognition of Faces: A Survey," Proc. the IEEE, Vol. 83, No. 5, pp. 705-740, 1995.

상세보기
M. Turk and A. Pentland, "Eigenfaces for Recognition," Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991.

상세보기
Tat-Jun Chin and D. Suter, A Study of the Eigenface Approach for Face Recognition, Technical Report of Monash University, Dept. of Elect & Computer Systems Engineering, pp. 1-18, 2004.
D. Blackburn, M. Bone, and P. Phillips, Face Recognition Vendor Test 2000: Evaluation Report, National Institute of Science and Technology, 2000.
P.J. Phillips, H. Moon, S.Rizvi, and P. Rauss, "FERET Evaluation Methodology for Face Recognition Algorithms," IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 22, Issue 10, pp. 1090-1103, 2000.

상세보기
D. Bryliuk and V. Starovoitov, "Access Control by Face Recognition using Neural Networks and Negative Examples," Proc. 2nd International Conference on Artificial Intelligence, pp. 428-436, 2002.
S.A. Nazeer, N. Omar, and M. Khalid, "Face Recognition System using Artificial Neural Networks Approach," Proc. IEEE International Conference on Signal Processing, Communications and Networking, pp. 420-425, 2007.
Huang, X. Shao, and H. Wechsler, Face Pose Discrimination Using Support Vector Machines, Technical report of George Mason University and University of Minnesota, Minneapolis Minnesota, Vol. 1, pp. 154-156. 1998.
R. Brunelli and T. Poggio, "Face Recognition: Features versus Templates," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 15, No.10, pp. 1042-1052, 1993.

상세보기
W. Chen, T. Sun, X. Yang, and L. Wang, "Face Detection based on Half Face Template," Proc. the IEEE Conference on Electronic Measurement and Instrumentation, pp. 54-58, 2009.
Dai Yang-chun and Xie Fang, "Face Recognition Based on the Fuzzy RBF Neural Network," Techniques of Automation and Applications, Vol. 25, No. 6, pp. 112-119, 2006.
Wenkai Xu and Eung-Joo Lee, "A Combinational Algorithm for Multi Face Recognition," International Journal of Advancements in Computing Technology, Vol. 4, No. 13, pp. 146-154, 2012.
A. Esposito, M. Marinaro, D. Oricchio, and S. Scarpetta, "Approximation of Continuous and Discontinuous Mappings by a Growing Neural RBF-based Algorithm," Neural Networks, Vol. 12, No. 1, pp. 651-665, 2000.
E.D. Virginia, "Biometric Identification System using a Radial Basis Network," Proc. IEEE International Conference of Security Technology, pp. 47-51, 2000.
M. Kirby and L. Sirovich, "Application of the Karhunen-Loeve Procedure for The Characterization of Human Faces," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 12, No. l, pp.103-108, 1990.

상세보기
J. Lu, N. Plataniotis Kostantinos, and N. Venetsanopoulos Anastasios, "Face Recognition using LDA-based Algorithm," IEEE Transactions on Neural Networks, Vol. 14, No. 1, pp. 195-200, 2003.

상세보기
A.M. Martinez and A.C. Kak., "PCA versus LDA," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, pp. 228- 233, 2001.

상세보기
Z. Liang and P.F. Shi, "Uncorrelated Discriminant Vectors using a Kernel Method," Pattern Recognition, Vol. 38, No. 2, pp. 307-310, 2005.

상세보기
N. Peter Belhumeur, P. Joao Hespanha, and David J. Kriegman, "Eigenfaces vs. Fisherfaces: Using Class Specific Linear Projection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 14, No. 2, pp. 239-256, 1997.
B. Marian, Stewart, R. Movellan Javier, and J. Sejnowski Terrence, "Face Recognition by Independent Component Analysis," IEEE Transactions on Neural Networks, Vol. 13, No. 6, pp.1450-1464, 2002.

상세보기
Meng Joo Er, Shiqian Wu, Juwei Lu, and Hock Lye Toh, "Face Recognition with Radial Basis Function Neural Networks", IEEE Transactions on Neural Networks, Vol. 13, No. 3, pp. 697-710, 2002.

상세보기
Wenkai Xu and Eung-Joo Lee, "Dynamic Human Activity Recognition Based on Improved FNN Model," Journal of Korea Multimedia Society, Vol. 15, No. 4, pp. 417-424, 2012.

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Face Recognition Based on Improved Fuzzy RBF Neural Network for Smar t Device 원문보기

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Face Recognition Based on Improved Fuzzy RBF Neural Network for Smar t Device 원문보기

Abstract ▼ AI-Helper

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