Facial feature extraction and recognition using Deformable Parts Model and DSIFT

In this paper, a novel method for facial feature extraction and recognition using an optimized combination of Deformable Parts Model (DPM) and Dense Scale Invariant Feature Transform (D-SIFT) is proposed. Real time face recognition systems pose challenges such as the speed and responsiveness. When the basic SIFT algorithm is applied to the entire face, the number and location of the detected keypoints changes with illumination in real time. Moreover, occlusion results in the generation of unwanted keypoints which decreases accuracy. In general, more time is consumed for detection of keypoints. These challenges are addressed by Dense SIFT algorithm, as the number of keypoints and their locations are controlled. DPM allows reduction in dimensionality of features by considering eyes, nose and mouth patches as Regions of Interest (ROI). These patches can be independently recognized. The D-SIFT features (descriptors), extracted from the ROIs, are classified using Support Vector Machines (SVM). The proposed method is tested with the self-created and Caltech databases. Experimental procedures show that the proposed method facilitates recognition with mean accuracy of 85% even in case of partial occlusions.