Abstract
Recently the Gabor-based features have been successfully used for face representation and recognition. In these methods, the face image is filtered with the multiscale multiorientation Gabor filter bank to generate multiple Gabor magnitude images (GMIs), and then the down-sampled GMIs or the LBP (local binary pattern) histograms of GMIs are stacked to form the feature. The stacking procedure makes the dimensions of these features very high, which causes extreme computing and storage load. In this paper, a novel Gabor-based feature termed Gabor orientation histogram (GOH) is proposed, which greatly reduces the feature dimension. Unlike stacking, GOH takes the structure underlying different GMIs into account by regarding the GMIs of different orientations at the same point as a whole, namely orientation vector, to represent the point. Moreover, GOH takes the structure of local region into account by calculating the orientation histogram based on the orientation vectors of points in the local region to describe the region, which is robust to local deformation and noises. The experimental results on the FERET and FRGC databases show that the proposed GOH reduces the feature extraction and recognition time significantly while retains the high recognition performance, which makes a progress toward the practical applications of Gabor-based features for face representation and recognition.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen LF, Liao HY, Ko MT, Lin JC, Yu GJ (2000) A new LDA-based face recognition system which can solve the small sample size problem. Pattern Recogn 33(10):1713–1726
Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of IEEE conference on computer vision and pattern recognition, vol. 1. pp 886–893
Daugman J (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J Opt Soc Am A: Opt 2(7):1160–1169
Gabor D (1946) Theory of communication. part 1: the analysis of information. J Inst Electr Eng Part II I: Radio Commun Eng 93(26):429–441
Jones J, Palmer L (1987) An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. J Neurophysiol 58(6):1233–1258
Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110
Morrone M, Burr D, Maffei L (1982) Functional implications of cross-orientation inhibition of cortical visual cells. i. neurophysiological evidence. In: Proceedings of the royal society B, vol. 216(1204). pp 335–354
Phillips P, Flynn P, Scruggs T, Bowyer K, et al (2005) Overview of the face recognition grand challenge. In: Proceedings of IEEE conference on computer vision and pattern recognition, vol. 1. pp 947–954
Phillips P, Hyeonjoon M, Rizvi S, Rauss P (2000) The FERET evaluation methodology for face-recognition algorithms. IEEE Trans Pattern Anal Mach Intell 22(10):1090–1104
Shan S, Zhang W, Su Y, Chen X, Gao W (2006) Ensemble of piecewise FDA based on spatial histograms of local (gabor) binary patterns for face recognition. In: Proceedings of 18th Int’l Conference on pattern recognition, vol. 4. pp 606–609
Watkins D, Berkley M (1974) The orientation selectivity of single neurons in cat striate cortex. Exp Brain Res 19(4):433–446
Zhang W, Shan S, Gao W, Chen X, Zhang H (2005) Local gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: Proceedings on Int’l Conference on computer vision, vol. 1. pp 786–791
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yi, J., Su, F. (2014). Gabor Orientation Histogram for Face Representation and Recognition. In: Farag, A., Yang, J., Jiao, F. (eds) Proceedings of the 3rd International Conference on Multimedia Technology (ICMT 2013). Lecture Notes in Electrical Engineering, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41407-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-642-41407-7_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41406-0
Online ISBN: 978-3-642-41407-7
eBook Packages: EngineeringEngineering (R0)