Face recognition using Kekre's wavelets energy & performance analysis of feature vector variants

Authors:
H. B. Kekre;V. A. Bharadi;P. P. Janrao;V. I. Singh
Affiliations:
MPSTME, NMIMS University, Mumbai, India;MPSTME, NMIMS University, Mumbai, India;TCET, Mumbai University, Mumbai, India;TCET, Mumbai University, Mumbai, India
Venue:
Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Year:
2011

Citing 8
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
Bayesian face recognition using Gabor features

WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Handbook of Biometrics

Handbook of Biometrics
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Face Recognition Using Multi-Resolution Transform

ICCIMA '07 Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007) - Volume 02
Shape-Driven Gabor Jets for Face Description and Authentication

IEEE Transactions on Information Forensics and Security
An introduction to biometric recognition

IEEE Transactions on Circuits and Systems for Video Technology

Quantified Score

Hi-index	0.00

Visualization

Abstract

Biometric authentication technologies are based on measurable physiological or psychological characteristics of human beings. Face is one of the important physiological biometrics traits. Wavelets are useful in multi-resolution analysis of images, they are very good option for analyzing texture feature of images. In this paper a new family of wavelets called as kekre's wavelet is used for multiresolutoin analysis of face images. Different variants of feature vectors are generated and their performance for face recognition is analyzed. The analysis shows that kekre's wavelets are faster than Haar wavelets and the feature vector based on these wavelets gives good accuracy.