Psychologically inspired dimensionality reduction for 2D and 3D Face Recognition

We present a number of related novel methods for reducing the dimensionality of data for the purposes of 2D and 3D face recognition. Results from psychology show that humans are capable of very good recognition of low resolution images and caricatures. These findings have inspired our experiments into methods of effective dimension reduction. For experimentation we use a subset of the benchmark FRGCv2.0 database as well as our own photometric stereo ``Photoface'' database. Our approaches look at the effects of image resizing, and inclusion of pixels based on percentiles and variance. Via the best combination of these techniques we represent a 3D image using only 61 variables and achieve 95.75% recognition performance (only a 2.25% decrease from using all pixels). These variables are extracted using computationally efficient techniques instead of more intensive methods employed by Eigenface and Fisherface techniques and can additionally reduce processing time tenfold.

[1]  Pawan Sinha,et al.  Face Recognition by Humans: Nineteen Results All Computer Vision Researchers Should Know About , 2006, Proceedings of the IEEE.

[2]  M K Unnikrishnan,et al.  How is the individuality of a face recognized? , 2009, Journal of theoretical biology.

[3]  Robert J. Woodham,et al.  Photometric method for determining surface orientation from multiple images , 1980 .

[4]  Christoph von der Malsburg,et al.  Strategies and Benefits of Fusion of 2D and 3D Face Recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[5]  Michael Kubovy,et al.  Caricature and face recognition , 1992, Memory & cognition.

[6]  De-Shuang Huang,et al.  Locally linear discriminant embedding: An efficient method for face recognition , 2008, Pattern Recognit..

[7]  Stefanos Zafeiriou,et al.  The Photoface database , 2011, CVPR 2011 WORKSHOPS.

[8]  Alejandro F. Frangi,et al.  Two-dimensional PCA: a new approach to appearance-based face representation and recognition , 2004 .

[9]  Yueting Zhuang,et al.  Hallucinating faces: LPH super-resolution and neighbor reconstruction for residue compensation , 2007, Pattern Recognit..

[10]  Takeo Kanade,et al.  Limits on Super-Resolution and How to Break Them , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Jieping Ye,et al.  Two-Dimensional Linear Discriminant Analysis , 2004, NIPS.

[12]  Melvyn L. Smith,et al.  3D face reconstructions from photometric stereo using near infrared and visible light , 2010, Comput. Vis. Image Underst..

[13]  Luuk J. Spreeuwers,et al.  The Effect of Image Resolution on the Performance of a Face Recognition System , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[14]  Ming-Hsuan Yang,et al.  Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[15]  Bruce A. Draper,et al.  A meta-analysis of face recognition covariates , 2009, 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems.

[16]  G. Rhodes,et al.  Identification and ratings of caricatures: Implications for mental representations of faces , 1987, Cognitive Psychology.

[17]  Ioannis A. Kakadiaris,et al.  Ethnicity- and Gender-based Subject Retrieval Using 3-D Face-Recognition Techniques , 2009, International Journal of Computer Vision.

[18]  Berk Gökberk,et al.  3D shape-based face representation and feature extraction for face recognition , 2006, Image Vis. Comput..

[19]  Thomas S. Huang,et al.  Image Super-Resolution Via Sparse Representation , 2010, IEEE Transactions on Image Processing.

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

[21]  Patrick J. Flynn,et al.  Face Recognition Using 2D and 3D Facial Data , 2003 .

[22]  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).

[23]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.