A Multi-Modal Approach to Frontal and Non Frontal Facial Feature Detection

Biometrics are measurable characteristics specific to an individual. Theycan be used to identify individuals. The use of biometrics for identification hasthe potential to make our lives easier, and the world we live in a safer place.While numerous different types of exploitable biometrics exist, facialidentification is highly pursued because the data can be captured at a distancewithout requiring the subject s active cooperation. While traditionally 2D imagesof faces have been used, 3D scans that contain both 3D data and registered colorare becoming easier to acquire.Before 3D face images can be used to identify an individual, they requiresome form of initial alignment information, typically based on facial featurelocations. This thesis proposes and analyzes a multimodal approach to automaticfacial feature detection. After beginning with a discussion on biometrics and therole of automatic facial feature detection, we provide a comparative evaluation of3D sensors. We follow this by a discussion of the algorithm s performance whenconstrained to frontal images and an analysis of its performance on a morecomplex dataset with significant head pose variation.