Open AccessFace Recognition by Reconstructing A 2.5 D Face using Photometric Stereo
Author(s) -
Bhavnesh Jaint,
Shivam Maheshwari,
Sushant Agarwal,
S. K. Singh
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.g5447.059720
Subject(s) - facial recognition system , computer science , artificial intelligence , three dimensional face recognition , face (sociological concept) , biometrics , computer vision , identification (biology) , photometric stereo , face detection , process (computing) , 3d single object recognition , stereo camera , face recognition grand challenge , pattern recognition (psychology) , feature extraction , image (mathematics) , cognitive neuroscience of visual object recognition , social science , botany , sociology , biology , operating system
Face recognition is an important application of image analysis and it has received a lot of interest in the last decade. There is a critical need for a reliable identification system. As of now, face recognition is not reliable enough in the majority of security applications, therefore a low cost, accurate, and viable identification method are required for face recognition. Two dimensional (2D) face recognition systems that are already existing are often not reliable. Three dimensional (3D) face recognition systems produce more accurate and robust than 2D systems but they are very costly due to large scanning and coded light and also consume a lot of time in the recognition process. This paper aims to produce a low-cost 3D face recognition system (2.5D) using photometric stereo which is less explored in face recognition systems. The capabilities of photometric stereo for use in face recognition are evaluated using a number of experiments conducted using the photometric stereo system and it is implemented and shown to be better than our traditional 2D systems. This system is aimed to solve a number of issues we see in face recognition systems like illumination, distance from the camera and pose and thus, it could be a useful application for biometric authentications in homes, governmental organizations and financial institutions