Face Recognition under Varying Illumination with Logarithmic Fractal Analysis

Face recognition under illumination variations is a challenging research area. This paper presents a new method based on the log function and the fractal analysis (FA) to produce a logarithmic fractal dimension (LFD) image which is illumination invariant. The proposed FA feature-based method is a very effective edge enhancer technique to extract and enhance facial features such as eyes, eyebrows, nose, and mouth. Our extensive experiments show the proposed method achieves the best recognition accuracy using one image per subject for training when compared to six recently proposed state-of-the-art methods.

[1]  Petros Maragos,et al.  Multiscale Fractal Analysis of Musical Instrument Signals With Application to Recognition , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

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

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

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

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

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

[7]  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..

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

[9]  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).

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

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

[12]  Fahima Nekka,et al.  The modified box-counting method: Analysis of some characteristic parameters , 1998, Pattern Recognit..

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

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

[15]  M. Fox,et al.  Fractal feature analysis and classification in medical imaging. , 1989, IEEE transactions on medical imaging.

[16]  Khan M. Iftekharuddin,et al.  Novel Fractal Feature-Based Multiclass Glaucoma Detection and Progression Prediction , 2013, IEEE Journal of Biomedical and Health Informatics.

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

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

[19]  Nirupam Sarkar,et al.  An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[20]  Omar S. Al-Kadi,et al.  Texture Analysis of Aggressive and Nonaggressive Lung Tumor CE CT Images , 2008, IEEE Transactions on Biomedical Engineering.

[21]  Christos Faloutsos,et al.  Fast feature selection using fractal dimension , 2010, J. Inf. Data Manag..

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

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