Facial Landmarking: Comparing Automatic Landmarking Methods with Applications in Soft Biometrics

Abstract

Registration is a critical step in computer-based image analysis. In this work we examine the effects of registration in face-based soft-biometrics. This form of soft-biometrics, better termed as facial analytics, takes an image containing a face and returns attributes of that face. In this work, the attributes of focus are gender and race. Automatic generation of facial analytics relies on accurate registration. Hence, this work evaluates three techniques for dense registration, namely AAM, Stacked ASM and CLM. Further, we evaluate the influence of facial landmark mis-localization, resulting from these techniques, on gender classification and race determination. To the best of our knowledge, such an evaluation of landmark mis-localization on soft biometrics, has not been conducted. We further demonstrate an effective system for gender and race classification based on dense landmarking and multi-factored principle components analysis. The system performs well against a multi-age face dataset for both gender and race classification.