Gabor feature constrained statistical model for efficient landmark localization and face recognition

Authors:
Sanqiang Zhao;Yongsheng Gao;Baochang Zhang
Affiliations:
School of Engineering, Griffith University, Nathan Campus, Brisbane, QLD 4111, Australia;School of Engineering, Griffith University, Nathan Campus, Brisbane, QLD 4111, Australia;School of Engineering, Griffith University, Nathan Campus, Brisbane, QLD 4111, Australia and School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China
Venue:
Pattern Recognition Letters
Year:
2009

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Abstract

Feature extraction and classification using Gabor wavelets have proven to be successful in computer vision and pattern recognition. Gabor feature-based Elastic Bunch Graph Matching (EBGM), which demonstrated excellent performance in the FERET evaluation test, has been considered as one of the best algorithms for face recognition due to its robustness against expression, illumination and pose variations. However, EBGM involves considerable computational complexity in its rigid and deformable matching process, preventing its use in many real-time applications. This paper presents a new Constrained Profile Model (CPM), in cooperation with Flexible Shape Model (FSM) to form an efficient localization framework. Through Gabor feature constrained local alignment, the proposed method not only avoids local minima in landmark localization, but also circumvents the exhaustive global optimization. Experiments on CAS-PEAL and FERET databases demonstrated the effectiveness and efficiency of the proposed method.