Octagonal prism LBP representation for face recognition

In this paper, we propose an octagonal prism representation for local binary patterns (LBP). This representation implements a new circular distance measurement for face recognition under various illumination conditions. The LBP method has been widely used in many computer vision applications, particularly for face recognition. Most LBP matching methods use distribution features with a bin-to-bin distance measure. However, using this bin-to-bin distance measure may produce low similarity scores even for similar patterns. To address this problem, we placed the LBPs on an octagonal prism in a three dimensional space and used the Euclidean distance measure. In the proposed octagonal prism representation, the LBPs were represented as three dimensional vectors on the octagonal prism. Since similar patterns under different illumination conditions are located in the vicinity on the octagonal prism, the proposed method proved robust against illumination variations. The proposed method produced noticeably improved performance when using the CMU PIE, Yale B, and Extended Yale B databases.

[1]  D. P. Huijsmans,et al.  CONTENT-BASED INDEXING PERFORMANCE: A CLASS SIZE NORMALIZED PRECISION, RECALL, GENERALITY EVALUATION , 2003 .

[2]  Xuan Zou,et al.  Illumination Invariant Face Recognition: A Survey , 2007, 2007 First IEEE International Conference on Biometrics: Theory, Applications, and Systems.

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

[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]  Meng Joo Er,et al.  Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[7]  Marko Heikkilä,et al.  A texture-based method for modeling the background and detecting moving objects , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Matti Pietikäinen,et al.  Optimising Colour and Texture Features for Real-time Visual Inspection , 2002, Pattern Analysis & Applications.

[9]  Wen Gao,et al.  Illumination normalization for robust face recognition against varying lighting conditions , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

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

[11]  Horst Bischof,et al.  A 3D Teacher for Car Detection in Aerial Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  Dorin Comaniciu,et al.  Total variation models for variable lighting face recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Lei Zhang,et al.  Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  David J. Kriegman,et al.  Acquiring linear subspaces for face recognition under variable lighting , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Wen Gao,et al.  Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition , 2007, IEEE Transactions on Image Processing.

[17]  Matti Pietikäinen,et al.  Extended local binary patterns for face recognition , 2016, Inf. Sci..

[18]  Loris Nanni,et al.  Ensemble of Multiple Pedestrian Representations , 2008, IEEE Transactions on Intelligent Transportation Systems.

[19]  Yang Gao,et al.  Local histogram specification for face recognition under varying lighting conditions , 2014, Image Vis. Comput..

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

[21]  Nicu Sebe,et al.  Content-based indexing performance: size normalized precision, recall, generality evaluation , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[22]  Biao Wang,et al.  Illumination Normalization Based on Weber's Law With Application to Face Recognition , 2011, IEEE Signal Processing Letters.

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

[24]  Guizhong Liu,et al.  Scale- and Rotation-Invariant Local Binary Pattern Using Scale-Adaptive Texton and Subuniform-Based Circular Shift , 2012, IEEE Transactions on Image Processing.

[25]  Xavier Lladó,et al.  False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns , 2007, MICCAI.

[26]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[27]  Matti Pietikäinen,et al.  Human Activity Recognition Using a Dynamic Texture Based Method , 2008, BMVC.

[28]  Wen Gao,et al.  A comparative study on illumination preprocessing in face recognition , 2013, Pattern Recognit..

[29]  Alfred Stein,et al.  Multivariate texture‐based segmentation of remotely sensed imagery for extraction of objects and their uncertainty , 2005 .

[30]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[31]  Xuelong Li,et al.  Gabor-Based Region Covariance Matrices for Face Recognition , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[32]  Haitao Wang,et al.  Face recognition under varying lighting conditions using self quotient image , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[33]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Yicong Zhou,et al.  Generalized Weber-face for illumination-robust face recognition , 2014, Neurocomputing.

[35]  Khoa N. Le.  A mathematical approach to edge detection in hyperbolic-distributed and Gaussian-distributed pixel-intensity images using hyperbolic and Gaussian masks , 2011, Digit. Signal Process..

[36]  Adrien Bartoli.  The Geometry of Dynamic Scenes - On Coplanar and Convergent Linear Motions Embedded in 3D Static Scenes , 2002, BMVC.

[37]  Yi-Ping Hung,et al.  Illumination Compensation Using Oriented Local Histogram Equalization and its Application to Face Recognition , 2012, IEEE Transactions on Image Processing.

[38]  Samy Bengio,et al.  A Discriminative Kernel-Based Approach to Rank Images from Text Queries , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Jian-Huang Lai,et al.  Face illumination normalization on large and small scale features , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[40]  Gregory K. Egan,et al.  On mathematical derivations of auto-term functions and signal-to-noise ratios of the Choi-Williams, first- and nth-order hyperbolic kernels , 2006, Digit. Signal Process..

[41]  W. Ali,et al.  Visual tree detection for autonomous navigation in forest environment , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[42]  Matti Pietikäinen,et al.  Visual Characterization of Paper Using Isomap and Local Binary Patterns , 2005, MVA.

[43]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..