This thesis is dedicated to exploring the potential of the 3D shape of the nasal region for face recognition. In comparison to other parts of the face, the nose has a number of distinctive features that make it attractive for recognition purposes. It is relatively stable over different facial expressions, easy to detect because of its salient convexity, and difficult to be intentionally cover up without attracting suspicion. In addition compared to other facial parts, such as forehead, chin, mouth and eyes, the nose is not vulnerable to unintentional occlusions causedby scarves or hair.Prior to undertaking a thorough analysis of the discriminative features of the 3D nasal regions, an overview of denoising algorithms and their impact on the 3D face recognition algorithms is first provided. This analysis, which is one of the first to address this issue, evaluates the performance of 3D holistic algorithms when various denoising methods are applied. One important outcome of this evaluation is to determine the optimal denoising parameters in terms of the overall 3D face recognition performance. A novel algorithm is also proposed to learn the statistics of the noise generated by the 3D laser scanners and then simulate it over the facepoint clouds. Using this process, the denoising and 3D face recognition algorithms’ robustness over various noise powers can be quantitatively evaluated.A new algorithm is proposed to find the nose tip from various expressions and self-occluded samples. Furthermore, novel applications of the nose region to align the faces in 3D is provided through two pose correction methods. The algorithms are very consistent and robust against different expressions, partial and self-occlusions.The nose’s discriminative strength for 3D face recognition is analysed using two approaches. The first one creates its feature sets by applying nasal curves to the depth map. The second approach utilises a novel feature space, based on histograms of normal vectors to the response of the Gabor wavelets applied to the nasal region. To create the feature spaces, various triangular and spherical patches and nasal curves are employed, giving a very high class separability.A genetic algorithm (GA) based feature selector is then used to make the feature space more robust against facial expressions. The basis of both algorithms is a highly consistent and accurate nasal region landmarking, which is quantitatively evaluated and compared with previous work. The recognition ranks provide the highest identification performance ever reported for the 3D nasal region. The results are not only higher than the previous 3D nose recognition algorithms, but also better than or very close to recent results for whole 3D face recognition.The algorithms have been evaluated on three widely used 3D face datasets, FRGC, Bosphorus and UMB-DB.
|Date of Award||15 Dec 2014|
|Supervisor||Adrian Evans (Supervisor)|
- 3D face recognition
- Face landmarking
- nasal region