Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition
Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition
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DOI：
10.1109/tip.2002.999679
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发表时间：
2002-04-01
影响因子：
10.6
通讯作者：
Wechsler, H
中科院分区：
文献类型：
--
作者：
Liu, CJ;Wechsler, H
介绍了一种新的用于人脸识别的Gabor-Fisher分类器。GFC方法对光照和面部表情的变化具有较强的鲁棒性，将增强的Fisher线性判别模型(EFM)应用于人脸图像的Gabor小波表示的增广Gabor特征向量。本文的创新之处在于：1)推导了一种增广的Gabor特征向量，通过同时考虑数据压缩和识别(泛化)性能，利用EFM进一步降维；2)针对多类问题提出了一种Gabor-Fisher分类器；3)广泛的性能评估研究。特别是，我们对应用于不同分类器的不同相似性度量进行了比较研究。我们还对不同的人脸识别方法进行了对比实验研究，包括新的GFC方法、Gabor小波方法、特征脸方法、FisherFaces方法、EFM方法、Gabor和特征脸方法的组合以及Gabor和FisherFaces方法的组合。通过对200个被试的600幅FERET正脸图像在不同光照和表情下的人脸识别实验，验证了该方法在人脸识别中的可行性。新的GFC方法仅使用62个特征就达到了100%的人脸识别准确率。
This paper introduces a novel Gabor-Fisher Classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the Enhanced Fisher linear discriminant Model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; 2) the development of a Gabor-Fisher classifier for multi-class problems; and 3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the Eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the Eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.