AI-BASED HUMAN FACIAL RECOGNITION USING DEEP LEARNING WITH YALE FACE DATASET

Abstract

Human Faces as a biometric feature are applied in multiple applications where both public and private organizations have preserved facial images for membership cards, passports, or personal identifications. They have been used as referential databases in investigations and match facial images of victims, witnesses, or offenders. Moreover, the widespread usages of smartphones and digital cameras have made it simple to share created facial images using social networks. Thus, Human Facial Recognitions (HFRs) have been an exciting and quickly expanding field of study. Real time applications include HFRs for identifications, access controls, forensics, and human-computer interactions. Though studies have been proposed for HFRs, there are areas that are wanting in implementations. Hence, this work attempts to fill up these gaps in HFRs with its suggested AI Based Recognitions of Faces (AIBRF). The schema uses Deep Learning (DL) techniques for identifying faces. The schema is trained using Yale Face dataset and achieves 99% accuracy in HRFs