Approximation algorithm based on greedy approach for face recognition with partial occlusion

The problem of partial occlusion in face recognition has received less attention over the last few years. Partial occlusion is an important challenge of the face recognition, in which certain parts of a face are hidden by the objects such as sunglasses, hats, scarves, and a mask that can cause significant degradation in the performance of the recognition system. This paper specifically addresses face recognition with partial occlusion. The proposed algorithm is an approximate version of conventional dynamic time warping (DTW), which is an exact algorithm and based on dynamic programming. An exact algorithm provides an exact result and involves huge computation efforts when there is a gallery with more images. Hence, a faster approximation algorithm based on greedy approach is proposed to solve the partially occluded face recognition problem by finding a near optimal solution with a guarantee on its performance. Many image processing applications are real-time and need a near-optimal solution. The proposed work has two contributions, the first one is in designing a string generation algorithm for converting a face into a sequence of strings and the second is designing an approximation algorithm based on greedy approach for matching strings. The proposed work uses standard face databases such as FEI, IAB, ORL, and Extended Yale-B for evaluating the effectiveness of the system.