Advances in Automated Image Categorization: Sorting Images using Person Recognition Techniques

Abstract

The core problem addressed by this thesis is to provide practical tools for automatic sorting and cataloging of a typical consumer collection of digital images. The thesis presents a complete system solution, comprising (i) automated detection of face regions in images; (ii) multiple automated face recognition modules; (iii) automated colour and texture analysis of regions peripheral to the detected face regions; (iv) a decision fusion module combining the outputs of each recognition or peripheral region analysis module and enabling an output measure of similarity between each detected face regions and a user selected reference. Each system component is implemented and tested independently. The complete system is then tested and initial results indicate that the combined performance of two independent face recognition modules (DCT and PCA) o®er measurable improvement over a single recognition module when applied to typical consumer collections of images. The analysis of peripheral regions using the colour correlogram is shown to further improve the accuracy of face recognition based modules and to enable more granular sorting of the images containing a particular individual based on distinctive hair features or clothing. Techniques to improve the robustness of the system to variations in illumination and facial pose are investigated, tested and verified. A technique to significantly accelerate the retraining of basis functions f or PCA-based face recognition is presented with initial test results. Several working computer and Web applications based on, and illustrating features of the core system components are described and documented in detail.