3D reconstruction and face recognition using kernel-based ICA and neural networks

Authors:
Shye-Chorng Kuo;Cheng-Jian Lin;Jan-Ray Liao
Affiliations:
Department of Electrical Engineering, National Chung Hsing University, Taichung City 402, Taiwan, ROC and Department of Computer Science and Information Engineering, Nan Kai University of Technolo ...;Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung County 411, Taiwan, ROC;Department of Electrical Engineering, National Chung Hsing University, Taichung City 402, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Kernel-based nonlinear characteristic extraction and classification algorithms are popular new research directions in machine learning. In this paper, we propose an improved photometric stereo scheme based on improved kernel-independent component analysis method to reconstruct 3D human faces. Next, we fetch the information of 3D faces for facial face recognition. For reconstruction, we obtain the correct normal vector's sequence to form the surface, and use a method for enforcing integrability to reconstruct 3D objects. We test our algorithm on a number of real images captured from the Yale Face Database B, and use three kinds of methods to fetch characteristic values. Those methods are called contour-based, circle-based, and feature-based methods. Then, a three-layer, feed-forward neural network trained by a back-propagation algorithm is used to realize a classifier. All the experimental results were compared to those of the existing human face reconstruction and recognition approaches tested on the same images. The experimental results demonstrate that the proposed improved kernel independent component analysis (IKICA) method is efficient in reconstruction and face recognition applications.