Sparse representation and fisher discriminant criterion based dimensionality reduction for face recognition

Abstract

Dimensionality Reduction(DR) is a very popular topic in the field of pattern recognition. Generally, Practical applications like face recognition, object classification, and text categorization include high dimensional data. However, Past research shows that high dimensional image may reside in a low dimensional manifold. Therefore, To understand high dimensional data efficiently dimensionality reduction is a necessary pre-processing step. Many linear, non-linear, neighborhood and kernel-based DR techniques are developed and demonstrated good results in face recognition. All these methods are less efficient in case of a large variation in facial expression, illumination, and pose in realtime face recognition. A few years back, a sparse representation(SR) based classifier(SRC) shown amazing results in classification. To get SR, more number of training samples required than the input image size. In face recognition, training data size is mostly less compare to input image size. So, Dimensionality reduction becomes compulsory in this case before applying SRC. Recently, sparsity-based DR methods such as SPP, SRC-DP, and SRC-FDC are developed and shown great results in real-world face recognition. SPP and SRCDP use sparse reconstruction residual which is not much useful in classification. To overcome this, SRC-FDC uses the Fisher discriminant criterion for better class separation, but it uses random initialization for the initial projection matrix P0. A new DR technique with proper initialization for initial matrix P0 called Initialized SRC-FDC is proposed.Experiments performed on Extended Yale B, CMUPIE, and Coil-20 dataset shows that Initialized SRC-FDC is more effective and efficient than the original SRC-FDC.