Vision-based overhead view person recognition

Person recognition is a fundamental problem faced in any computer vision system. This problem is relatively easy if the frontal view is available, however, it gets intractable in the absence of the frontal view. We have provided a framework, which tries to solve this problem using the top view of the person. A special scenario of "smart conference room" is considered. Although, not much information is available in the top view, we have shown that by making use of DTC and Bayesian networks the output of the various sensors can be combined to solve this problem. The results presented in the end show that we can do person recognition (pose independent) with 96% accuracy for a group of 12 people. For pose dependent case, we have achieved 100% accuracy. Finally we have provided a framework to achieve this in real time.