A machine learning approach to detect occluded faces in unconstrained crowd scene

Abstract

The face verification systems gained significant attention in the last few years due to the increased security concern in public and private places. Face detection is the most important and initial stage in the automatic face verification system. It helps to determine the existence of faces in an image and return the position and location of the face. The face verification system's accuracy depends on face detection. The human faces are not always frontal and have many variations, therefore, face detection is challenging in unconstrained scenarios. One main challenge of face detection is occlusion. The proposed work is an attempt to illustrate the cognitive informatics approach using machine learning and present an occluded face detection method. The proposed method uses Adaboost[1] machine learning approach. The Viola-Jones[2] algorithm along with free rectangular features[3] has been adopted in the proposed approach in order to detect faces. the machine learning methods require two operation namely training and testing. Two cascade classifiers are used in which one is trained on holistic faces and the second is trained on half occluded faces; both of the classifiers are used in parallel to work in unconfined scene. Additionally, for improvement the correctness and adeptness of the system, the skin color models are applied which are used for removing of the false positive detection. The experiment has been performed on FDDB[4] dataset. The results shows that the proposed method achieve desirable results in the detection of half occluded faces.