Face Recognition Using Classification-Based Linear Projections
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Research Article Face Recognition Using Classification-Based Linear Projections Moshe Butman1 and Jacob Goldberger2 1 Computer 2 School
Science Department, Bar-Ilan University, Ramat-Gan 52900, Israel of Engineering, Bar-Ilan University, Ramat-Gan 52900, Israel
Correspondence should be addressed to Moshe Butman, [email protected] Received 24 July 2007; Revised 17 January 2008; Accepted 19 February 2008 Recommended by C. Charrier Subspace methods have been successfully applied to face recognition tasks. In this study we propose a face recognition algorithm based on a linear subspace projection. The subspace is found via utilizing a variant of the neighbourhood component analysis (NCA) algorithm which is a supervised dimensionality reduction method that has been recently introduced. Unlike previously suggested supervised subspace methods, the algorithm explicitly utilizes the classification performance criterion to obtain the optimal linear projection. In addition to its feature extraction capabilities, the algorithm also finds the optimal distance-metric that should be used for face-image retrieval in the transformed space. The proposed face-recognition technique significantly outperforms traditional subspace-based approaches particulary in very low-dimensional representations. The method performance is demonstrated across a range of standard face databases. Copyright © 2008 M. Butman and J. Goldberger. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
1.
INTRODUCTION
In recent years, automatic face recognition has become one of the most active research fields in computer vision and a large number of different recognition algorithms have been developed. Face recognition algorithms can be categorized into feature-based, holistic-based and hybrid-matching algorithms. In feature-based methods, local features such as the eyes, nose, and mouth are first extracted and their locations and local description are fed into the recognition system (e.g., [1, 2]). Hybrid-matching methods use a combination of global and local features for face recognition (e.g., [3, 4]). In another aspect, face recognition algorithms can be categorized into 2D, 3D and multimodal algorithms [5]. A comprehensive survey of face-recognition algorithms is given by Zhao et al. [6]. The most successful approaches, however, seem to be those appearance-based methods that operate directly on the face images. An image is considered as a highdimensional vector, that is, a point in a high-dimensional vector space and the set of all faces is assumed to form a lowdimensional manifold. Following this paradigm, face image matching can be viewed as a two-step process of subspace projection followed by classification in the low-dimensional space (see [7] for a recent survey on face recognition in subspaces). In a simple yet successful approach, face recogni-
tion is implemented as a linear subspa
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