User Recognition Via Facial Parameters With Occlusion Using CNN

Abstract

The prime essence of occlusion based face detection has been derived across the world with a simple agenda to tackle the rise of frauds that security reinforcement or surveillance techniques provide concrete solution with bio metric database. Though the prime solution is by bio metric authentication via smart phones through facial recognition of users; still the misuse is a challenge as the user’s face is often partially covered or occluded. Therefore, face detection or rather authentication even when occluded has become very important to prevent cyber frauds. Traditional approaches in terms of facial bio metric authentication typically have comprised of image processing or machine learning steps: feature extraction, pooling, segmentation, flattening and recognition. The researchers of this paper propose a facial occlusion detection network utilizing Convolutional Neural Networks (CNN). The result showed that accuracy in our developed model is in the range of 88 to 91 percent which in competence with the reported algorithms till occlusion of face in terms of percentage is restricted up to 50 percent of the focus area and 40 percent of the complete face.