Human recognition using local 3D ear and face features

[Truncated abstract] The field of Biometrics is rapidly gaining popularity due to increasing breaches of traditional security systems and the decreasing costs of sensors. Among the bio- metric traits, the ear and the face are considered to be the most socially accepted due to their easy and non-intrusive data acquisition. Furthermore, their feature richness and physical proximity make them good candidates for fusion. However, occlusions due to the presence of hair and ornaments and deformations due to facial expressions pose great challenges for real-life applications of these two biometrics. These challenges are addressed in this dissertation through the development of efficient and robust algorithms for ear detection, ear data representation and finally, the combination of ear and face biometrics using robust fusion techniques. The dissertation is organized as a set of papers already published and/or submitted to journals or internationally refereed conferences. In this dissertation, a fast and fully automatic approach for detecting 3D ears from corresponding 2D and 3D profile images using a Cascaded AdaBoost algorithm is proposed. The classifiers are trained with three new Haar-like features and the detection is made using a 16 £ 24 detection window placed around the ear. The approach is significantly robust to hair, earrings and earphones and unlike other approaches, it does not require any assumption about the localization of the nose or the ear pit. The proposed ear detection approach achieves a detection rate of 99.9% on the UND-J Biometrics Database with 830 images of 415 subjects (the largest publicly available profile database) taking only 7.7 ms on average using a C + + implementation on a Core 2 Quad 9550, 2.83 GHz PC. For ear recognition, I initially proposed to apply the Iterative Closest Point (ICP) algorithm in a hierarchical manner: First with a low and then with higher resolution meshes of 3D ear data.