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Advances in Computer Science and Engineering

Advances in Computer Science and Engineering
Volume 6, Issue 1, Pages 1 - 32 (February 2011)

A NEW APPROACH IMPROVING THE QUALITY OF 3D FACE RECOGNITION
Ahmad Radman, Abdulsalam Alkholidi and Habib Hamam

Abstract:
Face recognition is one of the most intensively studied topics in the field of computer vision and pattern recognition. In this paper, two statistical models of facial shadow and shape, embedded within a shape-from-shading (SFS) algorithm, are used to reconstruct a 3D facial shape and albedo from 2D image. We developed an iterative process in a statistical setting using Hidden Markov Model (HMM) in Expectation Maximization (EM) algorithm and the Principal Components Analysis (PCA) algorithm to model the likelihood for the shadow regions and surface normal. Our method improves the statistical model results by applying some constraints with feedback error soft correction to decreasing the error that results from non-Lambertian, shadowing, non-symmetric and non- continuously. We show how our models can be trained by using normalized gray, normalized albedo, and surface normal data obtained from range images and how these models can be adapted to intensity images of faces by using constraints on the surface normal direction and albedo. We also test the technique on a variety of images to assess the facial shape recovering efficiency, accuracy and capability for the combination of global and local constraints. Comparison between SFS and statistical model is presented.

Keywords and phrases: 3D face recognition, shape-from-shading, global, local models, albedo estimation, expectation maximization.

Communicated by Tarek M. Sobh

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