Face Authentication for Access Control based on SVM using Class Characteristics
Face authentication for access control is a face
membership authentication which passes the person of the incoming
face if he turns out to be one of an enrolled person based on face
recognition or rejects if not. Face membership authentication belongs
to the two class classification problem where SVM(Support Vector
Machine) has been successfully applied and shows better performance
compared to the conventional threshold-based classification. However,
most of previous SVMs have been trained using image feature vectors
extracted from face images of each class member(enrolled
class/unenrolled class) so that they are not robust to variations in
illuminations, poses, and facial expressions and much affected by
changes in member configuration of the enrolled class
In this paper, we propose an effective face membership
authentication method based on SVM using class discriminating
features which represent an incoming face image-s associability with
each class distinctively. These class discriminating features are weakly
related with image features so that they are less affected by variations
in illuminations, poses and facial expression.
Through experiments, it is shown that the proposed face
membership authentication method performs better than the threshold
rule-based or the conventional SVM-based authentication methods and
is relatively less affected by changes in member size and membership.
[1] L. O'Gorman, "Comparing passwords, tokens, and biometrics for user
authentication," Proceedings of the IEEE, vol. 91, no. 12, pp, 2021-2040,
Dec 2003.
[2] W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld, "Face Recognition:
A Literature Survey," ACM Computing Surveys, pp. 399-458, 2003.
[3] S.Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2004
[4] Y. Adini, Y. Moses, and S. Ullman, "Face Recognition: The problem of
compensating for changes in illumination direction," IEEE Trans. on
Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 721-732,
July 1997.
[5] C. J. Burges, "A tutorial on Support Vector Machines for Pattern
Recognition," Data Mining and Knowledge discovery, 2, pp.121-167,
1998.
[6] C. Cortes, and V. Vapnik, "Support vector network," Machine Learning
20, pp. 273-297, 1995.
[7] P.J. Phillips, "Support vector machines applied to face recognition," Adv.
Neural Inform. Process. Syst. 11, pp. 803-809, 1998.
[8] G. Guo, S. Z. Li, and K. Chan, Face Recognition by Support Vector
Machines," 4th IEEE Int-l Conf. on Automatic Face and Gesture
Recognition, pp. 196-201, 2000.
[9] S. Pang, D. Kim, S. Y. Bang, "Membership authentication in the dynamic
group by face classification using SVM ensemble," Pattern Recognition
Letters, vol. 24, no 1-3, Jan. 2003.
[10] S. Pang, D. Kim, S. Y. Bang, "Face membership authentication using
SVM classification tree generated by membership-based LLE data
partition," Neural Networks, IEEE Transactions on, vol. 16, no. 2, pp.
436-446, March 2005.
[11] Q. Liu, W. Yan, H. Lu and S. Ma, "Occlusion Robust Face Recognition
with Dynamic Similarity Features," 18th IEEE Int-l Conf. Pattern
Recognition (ICPR-06), 2006.
[12] C. Chang and C. Lin, "LIBSVM: a Library for Support Vector Machines,"
http://www.csie.ntu.edu.tw/~cjlin/libsvm
[13] B. E. Boser, I. Guyon, and V. Vapnik, "A training algorithm for optimal
margin classifiers," Proc. 5th Annual Workshop on Computational
Learning Theory, pp. 144-152, ACM Press, 1992.
[14] J. Zou, Q. Ji, G. Nagy, "A Comparative Study of Local Matching
Approach for Face Recognition," IEEE Trans. Image Processing, vol. 16,
No. 10, October, 2007.
[15] L. Wiskott, J. M. Fellous, N. Kuiger, and C. von der Malsburg, "Face Recognition by
Elastic Bunch Graph Matching," Pattern Analysis and Machine Intelligence, IEEE
Transactions, vol. 19, pp. 775-779, July 1997.
[16] R. Gross and V. Brajovic, "An image preprocessing algorithm for illumination
invariant face recognition," In Audio- and Video-Based Biometric Person
Authentication, vol. 2688, pp. 10-18, June, 2003.
[1] L. O'Gorman, "Comparing passwords, tokens, and biometrics for user
authentication," Proceedings of the IEEE, vol. 91, no. 12, pp, 2021-2040,
Dec 2003.
[2] W. Zhao, R. Chellappa, J. Phillips, and A. Rosenfeld, "Face Recognition:
A Literature Survey," ACM Computing Surveys, pp. 399-458, 2003.
[3] S.Z. Li and A. K. Jain, Handbook of Face Recognition, Springer, 2004
[4] Y. Adini, Y. Moses, and S. Ullman, "Face Recognition: The problem of
compensating for changes in illumination direction," IEEE Trans. on
Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 721-732,
July 1997.
[5] C. J. Burges, "A tutorial on Support Vector Machines for Pattern
Recognition," Data Mining and Knowledge discovery, 2, pp.121-167,
1998.
[6] C. Cortes, and V. Vapnik, "Support vector network," Machine Learning
20, pp. 273-297, 1995.
[7] P.J. Phillips, "Support vector machines applied to face recognition," Adv.
Neural Inform. Process. Syst. 11, pp. 803-809, 1998.
[8] G. Guo, S. Z. Li, and K. Chan, Face Recognition by Support Vector
Machines," 4th IEEE Int-l Conf. on Automatic Face and Gesture
Recognition, pp. 196-201, 2000.
[9] S. Pang, D. Kim, S. Y. Bang, "Membership authentication in the dynamic
group by face classification using SVM ensemble," Pattern Recognition
Letters, vol. 24, no 1-3, Jan. 2003.
[10] S. Pang, D. Kim, S. Y. Bang, "Face membership authentication using
SVM classification tree generated by membership-based LLE data
partition," Neural Networks, IEEE Transactions on, vol. 16, no. 2, pp.
436-446, March 2005.
[11] Q. Liu, W. Yan, H. Lu and S. Ma, "Occlusion Robust Face Recognition
with Dynamic Similarity Features," 18th IEEE Int-l Conf. Pattern
Recognition (ICPR-06), 2006.
[12] C. Chang and C. Lin, "LIBSVM: a Library for Support Vector Machines,"
http://www.csie.ntu.edu.tw/~cjlin/libsvm
[13] B. E. Boser, I. Guyon, and V. Vapnik, "A training algorithm for optimal
margin classifiers," Proc. 5th Annual Workshop on Computational
Learning Theory, pp. 144-152, ACM Press, 1992.
[14] J. Zou, Q. Ji, G. Nagy, "A Comparative Study of Local Matching
Approach for Face Recognition," IEEE Trans. Image Processing, vol. 16,
No. 10, October, 2007.
[15] L. Wiskott, J. M. Fellous, N. Kuiger, and C. von der Malsburg, "Face Recognition by
Elastic Bunch Graph Matching," Pattern Analysis and Machine Intelligence, IEEE
Transactions, vol. 19, pp. 775-779, July 1997.
[16] R. Gross and V. Brajovic, "An image preprocessing algorithm for illumination
invariant face recognition," In Audio- and Video-Based Biometric Person
Authentication, vol. 2688, pp. 10-18, June, 2003.
@article{"International Journal of Information, Control and Computer Sciences:58678", author = "SeHun Lim and Sanghoon Kim and Sun-Tae Chung and Seongwon Cho", title = "Face Authentication for Access Control based on SVM using Class Characteristics", abstract = "Face authentication for access control is a face
membership authentication which passes the person of the incoming
face if he turns out to be one of an enrolled person based on face
recognition or rejects if not. Face membership authentication belongs
to the two class classification problem where SVM(Support Vector
Machine) has been successfully applied and shows better performance
compared to the conventional threshold-based classification. However,
most of previous SVMs have been trained using image feature vectors
extracted from face images of each class member(enrolled
class/unenrolled class) so that they are not robust to variations in
illuminations, poses, and facial expressions and much affected by
changes in member configuration of the enrolled class
In this paper, we propose an effective face membership
authentication method based on SVM using class discriminating
features which represent an incoming face image-s associability with
each class distinctively. These class discriminating features are weakly
related with image features so that they are less affected by variations
in illuminations, poses and facial expression.
Through experiments, it is shown that the proposed face
membership authentication method performs better than the threshold
rule-based or the conventional SVM-based authentication methods and
is relatively less affected by changes in member size and membership.", keywords = "Face Authentication, Access control, member ship
authentication, SVM.", volume = "2", number = "5", pages = "1606-6", }