Face recognition under illumination variations based on eight local directional patterns

Face recognition under varying illumination is a challenging task. This study proposes a modified version of local directional patterns (LDP), eight local directional patterns (ELDP), to produce an illumination insensitive representation of an input face image. The proposed ELDP code scheme uses Kirsch compass masks to compute the edge responses of a pixel's neighbourhood. Then, ELDP uses all the directional numbers to produce an illumination invariant image. The authors' extensive experiments show that the ELDP technique achieves an average recognition accuracy of 98.29% on the CMU-PIE face database and 100% on the Yale B face database, and clearly outperforms the state-of-the-art representative techniques.

[1]  Matti Pietikäinen,et al.  IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2009, TPAMI-2008-09-0620 1 WLD: A Robust Local Image Descriptor , 2022 .

[2]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

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

[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]  Yuan Yan Tang,et al.  Face Recognition Under Varying Illumination Using Gradientfaces , 2009, IEEE Transactions on Image Processing.

[6]  Andrea F. Abate,et al.  2D and 3D face recognition: A survey , 2007, Pattern Recognit. Lett..

[7]  Xiaojun Qi,et al.  An effective neutrosophic set-based preprocessing method for face recognition , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[8]  Xiaojun Qi,et al.  Face Recognition under Varying Illumination with Logarithmic Fractal Analysis , 2014, IEEE Signal Processing Letters.

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

[10]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  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..

[12]  R. Engelbrecht,et al.  DIGEST of TECHNICAL PAPERS , 1959 .

[13]  Oksam Chae,et al.  Local Directional Pattern (LDP) for face recognition , 2010, 2010 Digest of Technical Papers International Conference on Consumer Electronics (ICCE).

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

[15]  Oksam Chae,et al.  Local Directional Number Pattern for Face Analysis: Face and Expression Recognition , 2013, IEEE Transactions on Image Processing.

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

[17]  Joongkyu Kim,et al.  Retinex method based on adaptive smoothing for illumination invariant face recognition , 2008, Signal Process..

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

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

[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]  Jiashu Zhang,et al.  Face recognition with enhanced local directional patterns , 2013, Neurocomputing.

[22]  Matti Pietikäinen,et al.  Learning Discriminant Face Descriptor , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  R A Kirsch,et al.  Computer determination of the constituent structure of biological images. , 1971, Computers and biomedical research, an international journal.