A novel statistical model to evaluate the performance of EBGM based face recognition

Authors:
Munmun Chakraborty;Kunal Chanda;Debasis Mazumdar
Affiliations:
CDAC, Kolkata, India;CDAC, Kolkata, India;CDAC, Kolkata, India
Venue:
PerMIn'12 Proceedings of the First Indo-Japan conference on Perception and Machine Intelligence
Year:
2012

Citing 7
Cited 0

Adaptive pattern recognition and neural networks

Adaptive pattern recognition and neural networks
C4.5: programs for machine learning

C4.5: programs for machine learning
Face recognition by elastic bunch graph matching

Intelligent biometric techniques in fingerprint and face recognition
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Statistical Performance Evaluation of Biometric Authentication Systems Using Random Effects Models

IEEE Transactions on Pattern Analysis and Machine Intelligence
Age-Invariant Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
How features of the human face affect recognition: a statistical comparison of three face recognition algorithms

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

Pose, illumination, expression and other transitive and demographic variates present in the facial images have significant effects on the performance of face recognition system. A Gibbs sampler based statistical simulation algorithm is presented to evaluate the performance of EBGM based face recognition system. A new set of microscopic and stochastic image features are proposed which takes key role in determining the quality of facial images. Effects of these features on the performance of the EBGM based face recognition system are evaluated using an algorithm based on random effects model and Gibbs sampler.