Collective Face Recognition System

Abstract

Facial recognition systems are built on computer programs in order to analyse images of human faces for the purpose of identifying them. The programs take a facial image, detects the face extracts crops the image and stores it in the file called a "template." Using templates, the software then compares that image with another image and produces a score that measures how similar the images are to each other. Typical sources of images for use in facial recognition include video camera signals and preexisting photos such as those in driver’s databases. Facial recognition systems are computer-based security systems that are able to automatically detect and identify human faces. These systems depend on a recognition algorithm, such as Haar cascades. The first step for a facial recognition system is to recognize a human face and extract it for the rest of the scene. Next, the system measures nodal points on the face, such as the distance between the eyes, the shape of the cheekbones and other distinguishable features. Object Detection using Haar feature-based cascade classifiers is an effective object detection method proposed . It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect objects in other images. Experimental results suggest that the proposed Haar cascade approach provides a better representation and achieves lower error rates in face recognition.