title: Individual stable space: an approach to face recognition under uncontrolled conditions.
abstract: There usually exist many kinds of variations in face images taken under uncontrolled conditions, such as changes of pose, illumination, expression, etc. Most previous works on face recognition (FR) focus on particular variations and usually assume the absence of others. Instead of such a "divide and conquer" strategy, this paper attempts to directly address face recognition under uncontrolled conditions. The key is the individual stable space (ISS), which only expresses personal characteristics. A neural network named ISNN is proposed to map a raw face image into the ISS. After that, three ISS-based algorithms are designed for FR under uncontrolled conditions. There are no restrictions for the images fed into these algorithms. Moreover, unlike many other FR techniques, they do not require any extra training information, such as the view angle. These advantages make them practical to implement under uncontrolled conditions. The proposed algorithms are tested on three large face databases with vast variations and achieve superior performance compared with other 12 existing FR techniques.
authors.affiliation: School of Engineering and Information Technology, Deakin University, Melbourne, VIC 3125, Australia. xge@deakin.edu.au
authors.forename: Xin
authors.initials: X
authors.lastname: Geng
authors.forename: Zhi-Hua
authors.initials: ZH
authors.lastname: Zhou
authors.forename: Kate
authors.initials: K
authors.lastname: Smith-Miles
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity