Development of Novel Fast Block Based Trace Mean Correspondence Algorithm for Face Tracking

We propose a novel computationally efficient correspondence algorithm to identify the correspondence of similar features of the reference frame to search frame in the dynamic image sequence analysis for face tracking. The correspondence of the features between the set of images is the central problem in computer vision(CV), image analysis(IA) and pattern recognition(PR). In these areas correspondence of features is the combinatorial explosion problem or NP-hard, because of their exhaustive searching for features in the sequence of image frames. This correspondence has three major steps such as segmentation, feature extraction and matching. In this paper we propose block-based segmentation in which the frame which contains the face to be track can be segmented as window of size 20*20 which covers the face. Here we consider the window trace mean is the feature since this is rotation invariant and it covers all the rows and columns of the window and it reduces the dimensionality and finally for matching we used the minimum absolute difference method which does not involves more computations since no multiplication operation is involved. We restrict the searching space to [-3,+3] pixels horizontally and vertically in the search image. Experimental results show that this novel algorithm could achieve much higher computational reduction as compared with full search (FS), diamond search (DS) (Tham et al., 1998 ), cross diamond search (CDS)(Cheung & Po, 2002) and area based correspondence (Gowramma & Kumar, 2006) algorithm for face tracking image sequence while similar prediction accuracy is maintained and it is especially suitable for video conferencing and slow moving dynamic image sequence.