Effective segmented face recognition (SFR) for IoT

Abstract

Face recognition technology becoming pervasive in the fields of computer vision, image processing, and pattern recognition. However, face recognition accuracy rates will decrease if training is done on disguised images with covered objects on a face area. This paper aims to propose a state-of-the-art face recognition methodology which could be applied in Internet of Things (IoT) devices as an input source; then face segmentation and training process will be executed in the cloud via internet; the recognition result will be sent to the connected applications which determines a safety check for personal or public security. This paper focuses on implementation of face segmentation and training process for IoT. Face extraction from the background and disguised part is applied by Fully Convolutional Networks (FCN), and then deep convolution neural network is employed for face training and testing process. This algorithm has been experimented on a challengeable face dataset. The proposed face recognition system is applied to IoT services which have multiple applications, such as, personal home security and public library space management. Compared to recognition without face segmentation, the results of proposed methodology indicate a better accuracy regarding recognition rate.