Face Identification Based on Active Facial Patches Using Multi-Task Cascaded Convolutional Networks

Abstract

Face recognition technology is widely used for access control, security, identification, safeguarding, verification, timekeeping, and machine vision, etc. a new face identification algorithm referred to as Multi-Task Cascaded Convolutional Network (MTCCN) has emerged and has been widely used in high accuracy and efficiency in facial recognition, active facial patch identification framework face detection, selection of eyes, nose, lip, and eyebrow, identifying facial patches location and extraction of patches. This paper aims to discuss the recognition and identification of faces using layers of the Convolutional Neural Network (CNN). It is done to process camera frames as they appear and subsequent identification of the person. With three convolutional networks, MTCCN outperforms many face detection tests incredibly well, even though it maintains realtime performance. An active facial patch using MTCNN method is introduced for recognizing human faces in real time was developed, evaluated, and 97.62% of the time, the technique could recognize human faces correctly.