An Efficient Face Recognition through Combining Local Features and Statistical Feature Extraction

Abstract

AbstractThis paper proposes a hybrid method for face recognition using local features and statistical feature extraction methods. First, a dense set of local feature points are extracted in order to represent a facial image. Each local feature point is described by the keypoint descriptor defined by SIFT feature. Then, the statistical feature extraction methods, PCA and LDA, are applied to the set of local feature descriptors in order to find low dimensional features. With the obtained low dimensional feature vectors, we can conduct face recognition task efficiently using a simple classifier. Through computational experiments on benchmark data sets, we show that the proposed method is superior to the conventional PCA and LDA in the classification performance. In addition, we also show that the proposed method can achieve remarkable improvement in the processing time compared to the conventional keypoint matching methods proposed for local features.KeywordsFace RecognitionLocal FeatureSift FeatureGray Level IntensityConventional Statistical MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.