Optimization of LBP parameters

In this paper, we propose the optimal parameters of local binary patterns such as type of pattern, size of blocks in the feature space and distance measure for face recognition using a genetic algorithm. The genetic algorithm is able to optimize all these parameters quickly and to improve the recognition accuracy. We provide a comparative study of three types of local binary patterns (LBP, LGP and NRLBP) and four distance measures (L1, L2, χ2, EMD). The genetic algorithm is also used to optimize parameters such as dimension of histograms. Our results are tested on three different face databases which have the similar properties. We can set these optimal parameters into our face recognition system suitable for the next-generation of hybrid broadcast broadband television.

[1]  Guojun Lu,et al.  Evaluation of similarity measurement for image retrieval , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[2]  Andy Harter,et al.  Parameterisation of a stochastic model for human face identification , 1994, Proceedings of 1994 IEEE Workshop on Applications of Computer Vision.

[3]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[4]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[5]  Tong Zhang,et al.  Investigation of local and global features for face detection , 2011, 2011 IEEE Symposium On Computational Intelligence For Multimedia, Signal And Vision Processing.

[6]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Inho Choi,et al.  Local Transform Features and Hybridization for Accurate Face and Human Detection , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Milos Oravec,et al.  Universal biometric evaluation system: Framework for testing evaluation and comparison of biometric methods , 2011, 2011 18th International Conference on Systems, Signals and Image Processing.

[9]  Xudong Jiang,et al.  Human detection using Discriminative and Robust Local Binary Pattern , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[10]  David W. Jacobs,et al.  Approximate earth mover’s distance in linear time , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.