Face detection using correlation filters

Abstract

Cameras are ubiquitous and available all around us. As a result, images and videos are posted online in huge numbers. These images often need to be stored and analyzed. This requires the use of various computer vision applications that includes detection of human faces in these images and videos. The emphasis on face detection is evident from the applications found in everyday point and shoot cameras for a better focus, on social networking sites for tagging friends and family and for security situations which subsequently require face recognition or verification. This thesis focuses on detecting human faces in still images and video frames using correlation filters. These correlation filters are trained using a recent technique called Minimum Output Sum of Squared Error (MOSSE) developed by Bolme et al. Since correlation filters identify only a peak location, it only helps in localizing a single target point. In this thesis, I develop techniques to use this localization for detection of human faces of different scales and poses in uncontrolled background, location and lighting conditions. The goal of this research is to extend correlation filters for face detection and identify the scenarios where its potential is the most. The specific contributions of this work are the development of a novel face detector using correlation filters and the identification of the strengths and weaknesses of this approach. This approach is applied to an easy dataset and a hard dataset to emphasize the efficacy of correlations filters for face detection. This technique shows 95.6% accuracy in finding the exact location of the faces in images with controlled background and lighting. Although, the results on a hard dataset were not better than the OpenCV Viola and Jones face detector, it showed much better results, 81.5% detection rate compared to 69.43% detection rate by the Viola and Jones face detector, when tested on a customized dataset that was controlled for location change between training and test datasets. This result signifies the strength of a correlation based face detector in a specific scenario with uniform setting, such as a building entrance or an airport security gate.