Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Recognition of facial expressions using Gabor wavelets and learning vector quantization
Engineering Applications of Artificial Intelligence
Using Gabor Filters Features for Multi-Pose Face Recognition in Color Images
IITA '08 Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application - Volume 01
IEEE Transactions on Image Processing
Component-based face recognition with 3D morphable models
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
Manifold elastic net: a unified framework for sparse dimension reduction
Data Mining and Knowledge Discovery
Robust principal component analysis?
Journal of the ACM (JACM)
RASL: Robust Alignment by Sparse and Low-Rank Decomposition for Linearly Correlated Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Recovery of Subspace Structures by Low-Rank Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hi-index | 0.08 |
This paper presents an efficient face recognition method where enhanced local Gabor binary pattern histogram sequence has been used for efficient face feature extraction and generalized neural network with wavelet as activation function is being used for classification. In this method the face is first decomposed into multiresolution Gabor wavelets the magnitude responses of which are applied to enhanced local binary patterns. The efficiency has been significantly improved by combining two efficient local appearance descriptors named Gabor wavelet and enhanced local binary pattern with generalized neural network. Generalized neural network is a proven technique for pattern recognition and is insensitive to small changes in input data. The proposed method is robust-to-slight variation of imaging conditions and pose variations. Performance comparison with other existing techniques shows that the proposed technique provides better results in terms of false acceptance rate, false rejection rate, equal error rate and time complexity.