A hierarchical face recognition algorithm based on humanoid nonlinear least-squares computation

Face recognition is a critical component in many computer vision applications. Although now big data computing could bring high face recognition rate, it needs strong computing power, and normally working in the cloud. However, in many computer vision applications, especially a lot of front-end application, it needs to quickly and efficiently recognize faces. Inspired by human rapid and accurate identification of familiar faces, we think that there may be a class of fast computing mechanisms that play a role in human face recognition and thus improve the accuracy of recognition. In this paper, we study the nonlinear least-squares calculation in face recognition application, and find that it really can improve the recognition rate, and more importantly, it can deal with any combination of face features, such as "detail" and "holistic" features, obtaining a high recognition rate. Further more, we study Sparse Representation-based Classification in depth and find that some "detail" features, such as mouth, eyes, could be accurately identified by Sparse Representation. Then we propose a hierarchical face recognition algorithm by the use of nonlinear least-squares computation named HSRC. HSRC combines the components of face features using nonlinear least-squares and reduces the requirement of alignment and integrity and so on. And the results of these experiments prove that the face recognition rate can be considerably improved.