Linlin Shen

A review on Gabor wavelets for face recognition

Due to the robustness of Gabor features against local distortions caused by variance of illumination, expression and pose, they have been successfully applied for face recognition. The Facial Recognition Technology (FERET) evaluation and the recent Face Verification Competition (FVC2004) have seen the top performance of Gabor feature based methods. This paper aims to give a detailed survey of state of the art 2D face recognition algorithms using Gabor wavelets for feature extraction. Existing problems are covered and possible solutions are suggested.

Gabor wavelets and General Discriminant Analysis for face identification and verification

A novel and uniform framework for both face identification and verification is presented in this paper. The framework is based on a combination of Gabor wavelets and General Discriminant Analysis, and can be considered appearance based in that features are extracted from the whole face image. The feature vectors are then subjected to subspace projection. The design of Gabor filters for facial feature extraction is also discussed, which is seldom reported in the literature. The method has been tested extensively for both identification and verification applications. The FERET and BANCA face databases were used to generate the results. Experiments show that Gabor wavelets can significantly improve system performance whilst General Discriminant Analysis outperforms other subspace projection methods such as Principal Component Analysis, Linear Discriminant Analysis, and Kernel Principal Component Analysis. Our method has achieved 97.5% recognition rate on the FERET database, and 5.96% verification error rate on the BANCA database. This is a significantly better performance than that attainable with other popular approaches reported in the literature. In particular, our verification system performed better than most of the systems in the 2004 International Face Verification Competition, using the BANCA face database and specially designed test protocols.

Face authentication test on the BANCA database

This work details the results of a face authentication test (FAT2004) (http://www.ee.surrey.ac.uk/banca/icpr2004) held in conjunction with the 17th International Conference on Pattern Recognition. The contest was held on the publicly available BANCA database (http://www.ee.surrey.ac.uk/banca) according to a defined protocol (E. Bailly-Bailliere et al., June 2003). The competition also had a sequestered part in which institutions had to submit their algorithms for independent testing. 13 different verification algorithms from 10 institutions submitted results. Also, a standard set of face recognition software packages from the Internet (http://www.cs.colostate.edu/evalfacerec) were used to provide a baseline performance measure.

A Novel Eye Location Algorithm based on Radial Symmetry Transform

A novel and robust eye location algorithm is proposed in this paper. The algorithm is based on a low level, context free generalized symmetry transform. Once the regions of interest are detected, characteristics of eyes can be used to improve detection results. The algorithm is tested using 1460 face images of the BioID database and 2730 images of the BANCA database. A fully automatic face verification system has also been developed using this eye location algorithm. The system was one of the top performers in the 2004 International Face Verification Competition

Information theory for Gabor feature selection for face recognition

A discriminative and robust feature—kernel enhanced informative Gabor feature—is proposed in this paper for face recognition. Mutual information is applied to select a set of informative and nonredundant Gabor features, which are then further enhanced by kernel methods for recognition. Compared with one of the top performing methods in the 2004 Face Verification Competition (FVC2004), our methods demonstrate a clear advantage over existing methods in accuracy, computation efficiency, and memory cost. The proposed method has been fully tested on the FERET database using the FERET evaluation protocol. Significant improvements on three of the test data sets are observed. Compared with the classical Gabor wavelet-based approaches using a huge number of features, our method requires less than 4 milliseconds to retrieve a few hundreds of features. Due to the substantially reduced feature dimension, only 4 seconds are required to recognize 200 face images. The paper also unified different Gabor filter definitions and proposed a training sample generation algorithm to reduce the effects caused by unbalanced number of samples available in different classes.

3D Gabor wavelets for evaluating SPM normalization algorithm

A Gabor wavelets based method is proposed in this paper for evaluating and tuning the parameters of image registration algorithms. We propose a 3D local anatomical structure descriptor, namely the Maximum Responded Gabor Wavelet (MRGW), for measuring registration quality based on anatomical variability of registered images. The effectiveness of the descriptor is demonstrated through a practical application, using the variance of MRGW response to tune parameters of a nonlinear spatial normalization algorithm, which is part of the popular software package for medical image processing - the Statistical Parametric Mapping (SPM).

Gabor wavelets and kernel direct discriminant analysis for face recognition

A novel Gabor-Kernel face recognition method is proposed in this paper. This involves convolving a face image with a series of Gabor wavelets at different scales, locations, and orientations and extracting features from resulting Gabor filtered images. kernel discriminant analysis (KDDA) is then applied to the feature vectors for dimension reduction as well as class separability enhancement. A database of 600 frontal-view face images from the FERET face database is used to test the method. Experimental results demonstrate the advantage of KDDA over other Kernel methods such as kernel principal component analysis (KPCA) and general discriminant analysis (GDA). Significant improvements are also observed when features are extracted from Gabor filtered images instead of the original images. A 94% accuracy has been observed for the novel Gabor + KDDA method on the FERET database using a simple classifier, which could be further improved by employing a more complex classifier and distance measurer.

Combining Wavelets with HMM for Face Recognition

This paper describes face recognition algorithms that improve upon the original DCT based HMM face recogniser by using wavelet multiresolution analysis to extract observation sequences. In this approach a face image is divided into a number of overlapping subimages and wavelet decomposition is performed on each of the subimages. The ORL and our own face databases are used to test the algorithms and it is observed that our algorithms give better performance than the original. The face recognition algorithm is incorporated into a real-time face recognition system developed at the University of Nottingham.

Factored principal components analysis, with applications to face recognition

A dimension reduction technique is proposed for matrix data, with applications to face recognition from images. In particular, we propose a factored covariance model for the data under study, estimate the parameters using maximum likelihood, and then carry out eigendecompositions of the estimated covariance matrix. We call the resulting method factored principal components analysis. We also develop a method for classification using a likelihood ratio criterion, which has previously been used for evaluating the strength of forensic evidence. The methodology is illustrated with applications in face recognition.

Erratum to “3D Gabor wavelets for evaluating SPM normalization algorithm”

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