A probabilistic collaborative dictionary learning‐based approach for face recognition

Abstract Although Sparse Representation based Classifier (SRC), a non‐parametric model, can obtain an interesting result for pattern recognition , a reasonable interpretation has been lacked for its classification mechanism. What is more, the training samples are used as off‐the‐shelf dictionary directly in SRC, which can make the feature hidden in the training samples hard be extracted. At the same time, the complexity of the algorithm is increased because of too many atoms of the dictionary. The authors first explains in detail the classification mechanism of SRC from the view of probabilistic collaborative subspace and offer the process to improve the stability of the algorithm using the joint probability in the case of the multi‐subspace. Then, the authors introduce the dictionary learning (DL) and Fisher criterion into the model to further enhance the discrimination of the coding coefficient. In order to ensure the convexity of the discrimination term and further enhance the discrimination, the authors add the L21‐norm term into the Fisher discrimination term and offer the proof for its convexity. Finally, the experimental result on a series of benchmark databases, such as AR, Extended Yale B, LFW3D‐hassner, LFW3D‐sdm and LFW3D‐Dlib, show that PCDDL outperforms existing classical classification models.