Face recognition is a longstanding computer vision problem and a variety of methods have been proposed for face recognition over the past two decades. In this talk, I will first introduce the typical system pipeline, and historical algorithm landmarks, and recent progresses of face biometric. I also review face detection and alignment as two underestimated components that significantly affect the real-world performance of a face recognition system. Most importantly, I will overview the trend from traditional subspace learning to the emerging feature learning techniques and discuss how they are employed to boost face recognition performance. I briefly introduce the basic concept of subspace and feature learning, and show the key advantages and disadvantages of existing feature learning methods in different face recognition tasks. Lastly, I will discuss some open problems in feature learning to show how to further develop more advanced feature learning algorithms for real-world face recognition.

In this talk, I will overview the trend of metric learning techniques and discuss how they advance various face recognition applications. This talk includes three parts. First, I will briefly introduce the basic concept of metric learning, and show how they are used to improve the performance of different face recognition tasks in previous work. Second, I will introduce several our newly proposed metric learning methods include cost-sensitive metric learning, sparse reconstruction metric learning, locality repulsed metric learning, and discriminative deep metric learning. Then, I will present how these proposed metric learning methods are used to improve different face analysis tasks including face identification, face verification, video-based face recognition, single-sample face recognition, facial age estimation, kinship verification, and head pose estimation. Lastly, I will discuss some open problems to understand how to develop more advanced metric learning algorithms for human face analysis in the future.