Face Recognition with Biologically Motivated Boosted Features

Authors:
Erez Berkovich;Hillel Pratt;Moshe Gur
Affiliations:
Biomedical Engineering Department, Technion --- Israel Institute of Technology, Haifa, Israel 32000;Evoked Potentials Laboratory, Technion --- Israel Institute of Technology, Haifa, Israel 32000;Biomedical Engineering Department, Technion --- Israel Institute of Technology, Haifa, Israel 32000
Venue:
Cognitive Vision
Year:
2009

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Abstract

The current work presents a new face recognition algorithm based on novel biologically-motivated image features and a new learning algorithm, the Pseudo Quadratic Discriminant Classifier (PQDC). The recognition approach consists of construction of a face similarity function, which is the result of combining linear projections of the image features. In order to combine this multitude of features the AdaBoost technique is applied. The multi-category face recognition problem is reformulated as a binary classification task to enable proper boosting. The proposed recognition technique, using the Pseudo Quadratic Discriminant Classifier, successfully boosted the image features. Its performance was better than the performance of the Grayscale Eigenface and L,a,b Eigenface algorithms.