Real-Time Face Detection and Face Recognition: Study of Approaches

Abstract

This study demonstrates various face detection and recognition techniques which have been studied till now and compares them on basis of their merits and demerits, discusses their methodologies of working and put forward a core idea of how face detection is done, mentioning about very basic term, so that any person who is not that good in technology can understand it and dive deep into this field. Some of the face detection techniques we will look into will be geometric-based face detection, feature-based face detection, and Haar-like feature based-face detection, giving special emphasis on Haar-like features-based face detection. In face recognition techniques, we looked into the lazy learner’s face recognition approach, neural networks face recognition approach, and holistic face recognition approaches. Objective of the study is also to demonstrate about preparing a model which detects faces in a real-time environment. Face detection, nowadays, is the most primary check in any security system. So, automation in detecting faces will prove helpful. This model, rather than any other model, works on real-time data provided. Our model works on the fundamentals of the K-nearest neighbors algorithm, Haar cascade classifier (an object detection technique), and OpenCV (an open-source python library for computer vision, machine learning, and image processing).