Face Recognition Technology

Classifying Faces with Non-negative Matrix Factorization

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

Recently non-negative matrix factorization (NMF) has received a lot of attentions in information retrieval, computer vision and pattern recognition. NMF aims to find two non-negative matrices whose product can well approximate the original matrix. The sizes of these two matrices are usually smaller than the original matrix. This results in a compressed version of the original data matrix. The solution of NMF yields a natural parts-based representation for the data. When NMF is applied for data representation, a major disadvantage is that it fails to consider the geometric structure in the data. Non-negative Matrix Factorization is a promising approach for face recognition in that it is capable of extracting the local features by factorizing the nonnegative matrix into two nonnegative matrices.

Index Terms: Matlab, source, code, face, facial, recognition, NMF, non-negative, matrix, factorization.

Release 1.0 Date 2014.06.08
Major features:

• Face recognition based on Non-negative Matrix Factorization
• Multiple file selection
• Automatic feature extraction
• Fast and optimized implementation
• Easy and intuitive GUI
• Demo code (protected P-files) available for performance evaluation