Face Recognition Based on Improved LTP

Localized binary model (LBP) is an efficient local feature description operator. As a nonparametric description operator, it has received more and more attention and has achieved great success in the field of face recognition. In this paper, we introduce only the first-order non-directional feature of LBP operator, and introduce the high-order differential ULDP operator in four directions, and apply the preprocessing method to face recognition. In addition, the threshold for the LBP operator is completely dependent on the defects of the central pixels. In this paper, the local threshold model (ALTP) of the adaptive threshold is proposed. The threshold of the region is automatically generated by calculating the mean and variance of the local region pixels. The experimental results in several commonly used face databases show that ULDP and ALTP in this paper have good robustness to face recognition in nonconstrained environment, especially face recognition with illumination change. Keywords—face recognition; LTP; ALTP; FERET database

[1]  Robert Frischholz,et al.  BioID: A Multimodal Biometric Identification System , 2000, Computer.

[2]  P. Jonathon Phillips,et al.  Face recognition vendor test 2002 , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[3]  Xavier Maldague,et al.  Infrared face recognition: A literature review , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[4]  Xiaoou Tang,et al.  Facial Landmark Detection by Deep Multi-task Learning , 2014, ECCV.

[5]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[7]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[8]  Amos Fiat,et al.  How to Prove Yourself: Practical Solutions to Identification and Signature Problems , 1986, CRYPTO.

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

[10]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[11]  Kaoru Uchida,et al.  Frontiers of Image Processing Technologies and Applications. Biometrics Personal Identification and Its Application. , 2000 .

[12]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[13]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Lei Zhang,et al.  Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary , 2010, ECCV.

[15]  Hui Zeng,et al.  Local image region description using orthogonal symmetric local ternary pattern , 2015, Pattern Recognit. Lett..

[16]  Qiang Ji,et al.  Multi-view face and eye detection using discriminant features , 2007, Comput. Vis. Image Underst..

[17]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Harry Wechsler,et al.  The FERET database and evaluation procedure for face-recognition algorithms , 1998, Image Vis. Comput..

[19]  Zhenhua Guo,et al.  Rotation invariant texture classification using LBP variance (LBPV) with global matching , 2010, Pattern Recognit..