Algorithm using supervised subspace learning and non‐local representation for pose variation recognition

Abstract

Pose variation has been one of the challenges of face recognition. To solve this challenge, the authors propose a classification algorithm using supervised subspace learning and non‐local representation (SSLNR). In SSLNR, they first propose a supervised subspace learning algorithm (SSLA). SSLA includes three different terms. The first term is the difference term, which can reduce the intra‐class differences. The second term is the block‐diagonal regularisation term, which promotes the samples to be represented by intra‐class samples. The last one is the noise robust term. Then, the original samples are mapped to the learned subspace by using SSLA. Thus, the intra‐class differences of the samples mapped to the learned subspace are reduced. Finally, those mapped samples are classified by proposed non‐local constraint‐based extended sparse representation classifier. SSLNR is extensively evaluated using four databases, namely Georgia Tech, Label faces in the wild, FEI and CVL. Experimental results show that SSLNR achieves better performance than some state‐of‐the‐art algorithms, such as DARG and RRNN.