Personal Virtual Assistant

The Personal Virtual Assistant (PVA) is an innovative, inexpensive, and reliable virtual secretary. This user-friendly system will perform many of the tasks commonly performed by a traditional secretary. The PVA will eradicate such problems and it will be easy to train by updating the software. The foundation for this project has come from research previously conducted in computer vision. Advanced features of the PVA will include the ability to identify humans. Some of the problems associated with working with humans can range from forgetting to write an appointment to missing work on a regular basis. The PVA project will expand upon computer vision research and incorporate new features which will lead to an interactive virtual assistant. The system will offer communication and other solutions to users. This system can be implemented in various fields. For example, in education, it can be used to organize and ease student appointments with deans or chairs. In business, offices can use it in instead of a regular secretary. This assistant will never forget about the meeting and will never be tired. Those are only the basic features of the PVA. At the long term a global network of PVA’s can be created allowing them to communicate between each other without need for appearance in person

Enhanced iris recognition: Algorithms for segmentation, matching and synthesis

This thesis addresses the issues of segmentation, matching, fusion and synthesis in the context of irises and makes a four-fold contribution. The first contribution of this thesis is a post matching algorithm that observes the structure of the differences in feature templates to enhance recognition accuracy. The significance of the scheme is its robustness to inaccuracies in the iris segmentation process. Experimental results on the CASIA database indicate the efficacy of the proposed technique. The second contribution of this thesis is a novel iris segmentation scheme that employs Geodesic Active Contours to extract the iris from the surrounding structures. The proposed scheme elicits the iris texture in an iterative fashion depending upon both the local and global conditions of the image. The performance of an iris recognition algorithm on both the WVU non-ideal and CASIA iris database is observed to improve upon application of the proposed segmentation algorithm. The third contribution of this thesis is the fusion of multiple instances of the same iris and multiple iris units of the eye, i.e., the left and right iris at the match score level. Using simple sum rule, it is demonstrated that both multi-instance and multi-unit fusion of iris can lead to a significant improvement in matching accuracy. The final contribution is a technique to create a large database of digital renditions of iris images that can be used to evaluate the performance of iris recognition algorithms. This scheme is implemented in two stages. In the first stage, a Markov Random Field model is used to generate a background texture representing the global iris appearance. In the next stage a variety of iris features, viz., radial and concentric furrows, collarette and crypts, are generated and embedded in the texture field. Experimental results confirm the validity of the synthetic irises generated using this technique