Abstract
Face and gait recognition belong to the field of biometrics, a very active area of research in the computer vision and pattern recognition society, and face and gait are two typical physiological and behavioral biometrics, respectively. This chapter provides a survey on face and gait recognition and presents an overview of the face and gait recognition systems, where the key components are described and the two common approaches are introduced. The approaches are the model-based approach and the appearance-based approach. It reviews the fusion of face and gait for recognition and details several commonly used face and gait databases. The chapter presents various feature extraction algorithms for face and gait recognition, ranging from linear, nonlinear to multilinear subspace learning algorithms. The development of face or gait recognition algorithms largely depends on the availability of large and representative public databases of face images or gait sequences so that algorithms can be compared and advancements can be measured.
Haiping Lu
Professor of Machine Learning, Head of AI Research Engineering, and Turing Academic Lead
I am a Professor of Machine Learning. I develop translational AI technologies for better analysing multimodal data in healthcare and beyond.