Abstract

Facial recognition systems have been successfully applied in security, law-enforcement and human identification application, for automatically identifying a human in a digital image or a video frame. In a feature-based face recognition system using a set of features extracted from each of the prominent facial components, automatic and accurate localization of facial features is an essential pre-processing step. The active shape model (ASM) is a flexible shape model that was originally proposed to automatically locate a set of landmarks representing the facial features. Various improved versions of this model for facial landmark annotation have been developed for increasing the shape fitting accuracy at the expense of significantly increased computational complexity.
This thesis is concerned with developing a low-complexity active shape model by incorporating the energy compaction property of the discrete cosine transform (DCT). Towards this goal, the proposed ASM, which utilizes a 2-D profile based on the DCT of the local grey-level gradient pattern around a landmark, is first developed. The ASM is then utilized in a scheme of facial landmark annotation for locating facial features of the face in an input image. The proposed ASM provides two distinct advantages: (i) the use of a smaller number of DCT coefficients in building a compressed DCT profile significantly reduces the computational complexity, and (ii) the process of choosing the low-frequency DCT coefficients filters out the noise contained in the image. Simulations are performed to demonstrate the superiority of the proposed ASM over other improved versions of the original active shape model in terms of the fitting accuracy as well as in terms of the computational complexity. It is shown that the use of the proposed model in the application of facial landmark annotation significantly reduces the execution time without affecting the accuracy of the facial shape fitting.