Open AccessReview of Facial Recognition Techniques
Author(s) -
Harshit Nigam
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40077
Subject(s) - computer science , facial recognition system , biometrics , three dimensional face recognition , artificial intelligence , identification (biology) , face recognition grand challenge , field (mathematics) , face (sociological concept) , support vector machine , face detection , human–computer interaction , machine learning , pattern recognition (psychology) , sociology , pure mathematics , social science , botany , mathematics , biology
Facial Recognition, the biggest breakthrough in Biometric identification and security since fingerprints, uses an individual’s facial features to identify and recognize them. A technology that seems too farfetched taken straight from a science fiction novel is now available in smartphones in the palm of our hands. Facial Recognition has gained traction as the primary method of identification whether its mobile phones, smart security systems, ID verification or something as simple as login in a website. Recent strides in facial recognition technologies have made it possible to design, build and implement a facial recognition system ourself. Using Computer Vision and machine learning libraries like Facial Recognition and Dlib, we can create a robust system that can detect faces and then match and identify it with a database of pre-loaded facial data to successfully recognize them. This study conducted a literature review of these aforementioned technologies and various other advancements in the field of computer vision facial recognition by other scholars in their research papers. This paper analyzes domains to understand the working of these machine learning models and their different implementations in facial recognition systems. The research conducted by us during this review will be paramount in creating a proof-of-concept prototype facial recognition system. Keywords: DLib, Facial _Recognition, Machine Learning (ML), Deep Learning (DL), CNN, KNN, Face Detection, HOG, Support Vector Machine (SVM), Face Recognition.