Skin texture features for face recognition

Face recognition has been deployed in a wide range of important applications including surveillance and forensic identification. However, it still seems to be a challenging problem as its performance severely degrades under illumination, pose and expression variations, as well as with occlusions, and aging. In this thesis, we have investigated the use of local facial skin data as a source of biometric information to improve human recognition. Skin texture features have been exploited in three major tasks, which include (i) improving the performance of conventional face recognition systems, (ii) building an adaptive skin-based face recognition system, and (iii) dealing with circumstances when a full view of the face may not be avai'lable. Additionally, a fully automated scheme is presented for localizing eyes and mouth and segmenting four facial regions: forehead, right cheek, left cheek and chin. These four regions are divided into nonoverlapping patches with equal size. A novel skin/non-skin classifier is proposed for detecting patches containing only skin texture and therefore detecting the pure-skin regions. Experiments using the XM2VTS database indicate that the forehead region has the most significant biometric information. The use of forehead texture features improves the rank-l identification of Eigenfaces system from 77.63% to 84.07%. The rank-l identification is equal 93.56% when this region is fused with Kernel Direct Discriminant Analysis algorithm

A generic framework for colour texture segmentation

This thesis proposes a novel method to combine the colour and the texture for colour texture segmentation. The objective of this research work is to derive a framework for colour texture segmentation and to determine the contribution of colour in colour texture analysis. The colour texture processing is based on the feature extraction from colour-textured images. The texture features were obtained from the luminance plane along with the colour features from the chrominance planes. Based on the above mentioned approach, a method was developed for colour texture segmentation. The proposed method unifies colour and texture features to solve the colour texture segmentation problem. Two of the grey scale texture analysis techniques, Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) based filter approach were extended to colour images. An unsupervised fc-means clustering was used to cluster pixels in the chrominance planes. Non-parametric test was used to test the similarity between colour texture regions. An unsupervised texture segmentation method was followed to obtain the segmented image. The evaluation of the segmentation was based on the ROC curves. A quantitative estimation of colour and texture performance in segmentation was presented. The use of different colour spaces was also investigated in this study. The proposed method was tested using different mosaic and natural images obtained from VisTex and other predominant image database used in computer vision. The applications for the proposed colour texture segmentation method are, Irish Script On Screen (ISOS) images for the segmentation of the colour textured regions in the document, skin cancer images to identify the diseased area, and Sediment Profile Imagery (SPI) to segment underwater images. The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicated that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient