A Robust High Performance Face Recognition using Decorrelation of Local Features by Discrete Cosine Transforms

This paper proposes a novel method of face recognition using de-correlation of local features using Discrete Cosine Transforms (DCT). The impulse for the proposed idea is with the fact that histograms DC constituent of local Gabor binary patterns constitute low frequency components which will sparsely help in actual recognition, because information in face resides in low frequency bands and is similar to all the images and also when these histograms are concatenated, it becomes difficult to differentiate and segregate actual frequency variations which add value for accurate recognition. A high correlation exists in between these histograms. This high correlation affects the recognition accuracy, hence de-correlation is achieved with the help of DCT application for individual histogram bins which aids in identification of actual frequency variations and highlights the changes in between two histograms thus improving the recognition accuracy. This method employs a non-statistical procedure which avoids training step for face samples thereby avoiding generalizability problem which is caused due to statistical learning procedure. The performance modeling is carried out by varying both internal and external factors of face recognition system and improvement is shown considerably high in terms of recognition accuracy and reduction in storage space by storing train images in compressed domain.