167 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 10 DOI: 10.4018/978-1-60960-477-6.ch010 INTRODUCTION In the last couple of decades, automatic comput- erised face recognition techniques have been de- veloped for the security of accessing confidential information in both the internet virtual world and the real world. Recognition systems are required to achieve high performance in a variety of dif- ferent environments such as wide camera angles, different illuminations and various expressions. Identity subspace learning techniques have improved significantly which can be traced back to Eigen-faces (Turk, 1991) designed for face recognition. Linear Discriminate Analysis (LDA) (Belhumeur, 1997), Active Appearance Models (AAM) (Cootes, 2001), Independent Hui Fang Swansea University, UK Nicolas Costen Manchester Metropolitan University, UK Phil Grant Swansea University, UK Min Chen Swansea University, UK Advances in Moving Face Recognition ABSTRACT This chapter describes the approaches to extracting features via the motion subspace for improving face recognition from moving face sequences. Although the identity subspace analysis has achieved reasonable recognition performance in static face images, more recently there has been an interest in motion-based face recognition. This chapter reviews several state-of-the-art techniques to exploit the motion informa- tion for recognition and investigates the permuted distinctive motion similarity in the motion subspace. The motion features extracted from the motion subspaces are used to test the performance based on a verifcation experimental framework. Through experimental tests, the results show that the correlations between motion eigen-patterns signifcantly improve the performance of recognition.