Face Recognition based on Higher-Order Statistics

Identification of people by face is the most effective non-intrusive method in biometry. However, it is a great challenge for researchers because faces are complex and multidimensional. In addition, high level of difficulty is added by changes in illumination and/or pose. In this work, a face recognition method based on Higher-Order Statistics (HOS) is proposed. HOS has the important signal processing properties of: (i) handling colored Gaussian measurement noise more efficiently, (ii) extracting information due to deviations from Gaussianity, and (iii) detecting and characterizing nonlinear properties in signals. In this work, features based on HOS are used to build compact signature of faces. It is considered a Public Security scenario where the goal is to detect and identify individuals with criminal links, previously registered in a database. To select the most discriminant HOS-based features, the Fisher's Discriminant Ratio (FDR) was used and the linear correlation was applied to eliminate redundancy. Three classifiers (the Bayesian, Support Vector Machines (SVM) and the K-nearest neighbor (KNN)) were employed for final classification and their performances were compared. For performance evaluation, images from ORL dataset were used. The results showed detection and classification rates over 70% and indicates the potential of HOS on building face signatures.