REaL: Real-time Face Detection and Recognition Using Euclidean Space and Likelihood Estimation

TL;DR

The paper introduces REaL, a real-time face detection and recognition system that efficiently processes live images using Euclidean space and likelihood estimation, achieving promising recognition rates and noise reduction.

Contribution

It presents a novel real-time face recognition system that improves computational efficiency and accuracy by using Euclidean space and likelihood estimation techniques.

Findings

Successful recognition on live images

Effective removal of non-human objects

High recognition accuracy in real-time

Abstract

Detecting and recognizing faces accurately has always been a challenge. Differentiating facial features, training images, and producing quick results require a lot of computation. The REaL system we have proposed in this paper discusses its functioning and ways in which computations can be carried out in a short period. REaL experiments are carried out on live images and the recognition rates are promising. The system is also successful in removing non-human objects from its calculations. The system uses a local database to store captured images and feeds the neural network frequently. The captured images are cropped automatically to remove unwanted noise. The system calculates the Euler angles and the probability of whether the face is smiling, has its left eye, and right eyes open or not.