Illumination Invariant Face Alignment Using Multi-band Active Appearance Model

Abstract

In this study, we present a new multi-band image representation for improving AAM segmentation accuracy for illumination invariant face alignment. AAM is known to be very sensitive to the illumination variations. We have shown that edges, originating from object boundaries are far less susceptible to illumination changes. Here, we propose a contour selector which mostly collects contours originating from boundaries of the face components (eyes, nose, chin, etc.) and eliminates the others arising from texture. Rather than representing the image using grey values, we use Hill, Hue and Grey value (HHG) for image representation. We demonstrate that HHG representation gives more accurate and reliable results as compared to image intensity alone under various lighting conditions.