An Improved Face Recognition Algorithm Using Histogram-Based Features in Spatial and Frequency Domains

Authors: Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

Abstract:

In this paper, we propose an improved face recognition algorithm using histogram-based features in spatial and frequency domains. For adding spatial information of the face to improve recognition performance, a region-division (RD) method is utilized. The facial area is firstly divided into several regions, then feature vectors of each facial part are generated by Binary Vector Quantization (BVQ) histogram using DCT coefficients in low frequency domains, as well as Local Binary Pattern (LBP) histogram in spatial domain. Recognition results with different regions are first obtained separately and then fused by weighted averaging. Publicly available ORL database is used for the evaluation of our proposed algorithm, which is consisted of 40 subjects with 10 images per subject containing variations in lighting, posing, and expressions. It is demonstrated that face recognition using RD method can achieve much higher recognition rate.

Keywords: Face recognition, Binary vector quantization (BVQ), Local Binary Patterns (LBP), DCT coefficients.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1112065

References:

[1] R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: a survey,” Proc. of IEEE, vol. 83, no. 5, 1995, pp.705-740.
[2] S. Z. Li and A. K. Jain, “Handbook of face recognition,” Springer, New York, 2005.
[3] R. Brunelli and T. Poggio, “Face recognition: features versus templates,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, 1993, pp. 1042-1052.
[4] L. Wiskott, J. M. Fellous, N. Kruger, and C. Malsburg, “Face recognition by elastic bunch graph matching,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 10, 1997, pp.775-780.
[5] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, 1991, pp. 71-86.
[6] W. Zhao, “Discriminant component analysis for face recognition,” Proc. ICPR’00, Track 2, 2000, pp. 822-825.
[7] K.M. Lam, H. Yan, “An analytic-to-holistic approach for face recognition based on a single frontal view,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 7, 1998, pp. 673-686.
[8] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. on Neural Networks, vol. 13, no. 6, 2002, pp. 1450-1464.
[9] B. Moghaddam and A. Pentland, “Probabilistic visual learning for object representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, 1997, pp. 696-710.
[10] S. G. Karungaru, M. Fukumi, and N. Akamatsu, “Face recognition in colour images using neural networks and genetic algorithms,” Int’l Journal of Computational Intelligence and Applications, vol. 5, no. 1, 2005, pp. 55-67.
[11] P. S. Penev and J. J. Atick, “Local feature analysis: a general statistical theory for object representation,” Network: Computation in Neural Systems, vol. 7, no. 3, 1996, pp. 477-500.
[12] W. B. Pennebaker and J. L. Mitchell, “JPEG still image data compression standard,” Van Nostrand Reinhold, New York, 1993.
[13] H. B. Kekre, T. K. Sarode, P. J. Natu, and S. J. Natu, “Transform based face recognition with partial and full feature vector using DCT and Walsh transform,” Proc. of the Int’l Conf. & Workshop on Emerging Trends in Technology, 2011, pp. 1295-1300.
[14] Z. Liu and C. Liu, “Fusion of color, local spatial and global frequency information for face recognition,” Pattern Recognition, vol. 43, Issue 8, Aug. 2010, pp. 2882-2890.
[15] H. F. Liau, K. P. Seng, L. M. Ang, and S. W. Chin, “New parallel models for face recognition,” Recent Advances in Face Recognition, Edited by K. Delac etc., In Tech, 2008, pp. 15-26.
[16] R. Tjahyadi, W. Liu, S. An and S. Venkatesh, “Face recognition via the overlapping energy histogram,” Int’l Joint Conf. on Artificial Intelligence, 2007, pp. 2891-2896.
[17] D. Zhong and I. Defee, “Pattern recognition in compressed DCT domain,” Proc. of Int’l Conf. on Image Processing, vol. 3, 2004, pp. 2031 - 2034.
[18] Z. M. Hafed and M. D. Levine, “Face recognition using the Discrete Cosine Transform,” Int’l Journal of Computer Vision, vol. 43, no. 3, 2001, pp. 167-188.
[19] S. Eickeler, S. Müller and G. Rigoll, “Recognition of JPEG compressed face images based on statistical methods,” Image and Vision Computing Journal, Special Issue on Facial Image Analysis, vol. 18, no. 4, Mar. 2000, pp. 279-287.
[20] S. Eickeler, S. Müller, and G. Rigoll, “High quality face recognition in JPEG compressed images,” Proceeding of Int’l Conf. on Image Processing, vol. 1, Oct. 1999, pp. 672-676.
[21] V. Nefian and M. H. Hayes, “Hidden Markov models for face recognition,” Int’l Conf. on Acoustics, Speech, and Signal Processing, May 1998, pp. 2721-2724.
[22] M. Shneier and M Abdel-Mottaleb, “Exploiting the JPEG compression scheme for image retrieval,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, Aug. 1996, pp. 849-853.
[23] A. Jain, Fundamentals of Digital Image Processing, Prentice: Englewood Cliffs, NJ, 1989.
[24] A. Gersho and R. M. Gray, “Vector quantization and signal compression,” Kluwer Academic, 1992.
[25] AT&T Laboratories Cambridge, “The database of faces,” at http://www.cl.cam.ac.uk/research/dtg/attarchive/ facedatabase. html
[retrieved: Dec. 2013].
[26] F. Samaria and A. Harter, “Parameterisation of a stochastic model for human face identification,” 2nd IEEE Workshop on Applications of Computer Vision, 1994, pp. 138-142.
[27] Q. Chen, K. Kotani, F. F. Lee, and T. Ohmi, “Face recognition using VQ Histogram in compressed DCT domain,” Journal of Convergence Information Technology, vol. 7, no. 1, 2012, pp. 395-404.
[28] Q. Chen, K. Kotani, F. F. Lee, and T. Ohm, “Face Recognition Using Histogram-based Features in Spatial and Frequency Domains”, Proceeding of the Sixth International Conference on Advances in Multimedia (MMEDIA 2014), 2014, pp. 53-57.
[29] T. Ojala, M. Pietikainen, T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on　Pattern Analysis and Machine Intelligence, vol. 24, no. 7, 2002, pp. 971-987.
[30] K. Kotani, Q. Chen, F. F. Lee, and T. Ohmi “Region-division VQ histogram method for human face recognition,” Intelligent Automation and Soft Computing, vol. 12, no. 3, 2006, pp. 257-268.