Optimized projection for Collaborative Representation based Classification and its applications to face recognition

• A new dimensionality reduction method called OP-CRC is proposed.
• OP-CRC is designed based on Collaborative Representation based Classification (CRC).
• The projection matrix of OP-CRC is solved by iteration algorithm.
• OP-CRC is effective for face recognition.

Collaborative Representation based Classification (CRC) is powerful for face recognition and has lower computational complexity than Sparse Representation based Classification (SRC). To improve the performance of CRC, this paper proposes a new dimensionality reduction method called Optimized Projection for Collaborative Representation based Classification (OP-CRC), which has the direct connection to CRC. CRC uses the minimum reconstruction residual based on collaborative representation as the decision rule. OP-CRC is designed according to this rule. The criterion of OP-CRC is maximizing the collaborative representation based between-class scatter and minimizing the collaborative representation based within-class scatter in the transformed space simultaneously. This criterion is solved by iterative algorithm and the algorithm converges fast. CRC performs very well in the transformed space of OP-CRC. Experimental results on Yale, AR, FERET, CMU_PIE and LFW databases show the effectiveness of OP-CRC in face recognition.