Weighted Piecewise LDA for Solving the Small Sample Size Problem in Face Verification

A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (...

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Veröffentlicht in:IEEE transactions on neural networks Jg. 18; H. 2; S. 506 - 519
Hauptverfasser: Kyperountas, M., Tefas, A., Pitas, I.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.03.2007
Schlagworte:
Algorithms
Artificial Intelligence
Biometrics
Biometry - methods
Classification
Computer Simulation
Discriminant Analysis
Face
Face - anatomy & histology
Face verification
Humans
Hyperplanes
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Informatics
Information security
Information Storage and Retrieval - methods
Linear discriminant analysis
linear discriminant analysis (LDA)
Linear Models
Neural networks
Pattern analysis
Pattern recognition
Pattern Recognition, Automated - methods
Phases
Sample Size
Similarity
small sample size (SSS) problem
System testing
ISSN:1045-9227
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Zusammenfassung:A novel algorithm that can be used to boost the performance of face-verification methods that utilize Fisher's criterion is presented and evaluated. The algorithm is applied to similarity, or matching error, data and provides a general solution for overcoming the "small sample size" (SSS) problem, where the lack of sufficient training samples causes improper estimation of a linear separation hyperplane between the classes. Two independent phases constitute the proposed method. Initially, a set of weighted piecewise discriminant hyperplanes are used in order to provide a more accurate discriminant decision than the one produced by the traditional linear discriminant analysis (LDA) methodology. The expected classification ability of this method is investigated throughout a series of simulations. The second phase defines proper combinations for person-specific similarity scores and describes an outlier removal process that further enhances the classification ability. The proposed technique has been tested on the M2VTS and XM2VTS frontal face databases. Experimental results indicate that the proposed framework greatly improves the face-verification performance
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ISSN:1045-9227
DOI:10.1109/TNN.2006.885038