Face Recognition Technology

Independent Component Analysis

Download now Matlab source code
Requirements: Matlab, Matlab Image Processing Toolbox.

In a task such as face recognition, much of the important information may be contained in the high-order relationships among the image pixels. A number of face recognition algorithms employ principal component analysis (PCA), which is based on the second-order statistics of the image set, and does not address high-order statistical dependencies such as the relationships among three or more pixels. Independent component analysis (ICA) is a generalization of PCA which separates the high-order moments of the input in addition to the second-order moments. ICA was performed on a set of face images by an unsupervised learning algorithm derived from the principle of optimal information transfer through sigmoidal neurons. The algorithm maximizes the mutual information between the input and the output, which produces statistically independent outputs under certain conditions. ICA representation was superior to representations based on principal components analysis for recognizing faces across sessions and changes in expression.

Index Terms: Matlab, source, code, face, recognition, ICA, independent, component, analysis.

Release 1.0 Date 2011.02.17
Major features:

• Face recognition based on ICA
• High recognition rate
• Fast and optimized implementation
• Easy and intuitive GUI
• Demo code (protected P-files) available for performance evaluation