Understanding the Role of Facial Asymmetry in Human Face Identification

Sinjini Mitra, Nicole Lazar, and Yanxi Liu
Statistics and Computing, Vol. 17, January, 2007, pp. 57 - 70.

  • Adobe portable document format (pdf) (515KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Face recognition has important applications in forensics (criminal identification) and security (biometric authentication). The problem of face recognition has been extensively studied in the computer vision community, from a variety of perspectives. A relatively new development is the use of facial asymmetry in face recognition, and we present here the results of a statistical investigation of this biometric. We first show how facial asymmetry informa- tion can be used to perform three different face recognition tasks--human identification (in the presence of expression variations), classification of faces by expression, and clas- sification of individuals according to sex. Initially, we use a simple classification method, and conduct a feature anal- ysis which shows the particular facial regions that play the dominant role in achieving these three entirely different clas- sification goals. We then pursue human identification under expression changes in greater depth, since this is the most important task from a practical point of view. Two different ways of improving the performance of the simple classifier are then discussed: (i) feature combinations and (ii) the use of resampling techniques (bagging and random subspaces). With these modifications, we succeed in obtaining near per- fect classification results on a database of 55 individuals, a statistically significant improvement over the initial results as seen by hypothesis tests of proportions.

Associated Center(s) / Consortia: Vision and Autonomous Systems Center and Medical Robotics Technology Center
Associated Lab(s) / Group(s): Human Identification at a Distance, Computational Symmetry, Biomedical Image Analysis
Associated Project(s): Facial Asymmetry as a Biometric
Number of pages: 14

Text Reference
Sinjini Mitra, Nicole Lazar, and Yanxi Liu, "Understanding the Role of Facial Asymmetry in Human Face Identification," Statistics and Computing, Vol. 17, January, 2007, pp. 57 - 70.

BibTeX Reference
   author = "Sinjini Mitra and Nicole Lazar and Yanxi Liu",
   title = "Understanding the Role of Facial Asymmetry in Human Face Identification",
   journal = "Statistics and Computing",
   pages = "57 - 70",
   month = "January",
   year = "2007",
   volume = "17",