计算机工程与应用2016，Vol.52Issue(14)：136-141,196,7.DOI:10.3778/j.issn.1002-8331.1409-0076
多尺度非监督特征学习的人脸识别
Multi-scale unsupervised feature learning for face recognition
摘要
Abstract
In order to fully utilize latent information of human face, a method called Multi-Scale Convolutional Auto-Encoder(MSCAE)is proposed. MSCAE extracts image’s multi-scale features using different sizes of convolution kernels. Since the new features reflect natural facial contents, human face can be restored better. The MSCAE applies a hierarchy of alternating filtering and sub sampling, and it makes features invariant to deformations including rotation, translation, and scale. The form of encoder-decoder is introduced to train the MSCAE so as to obtain the feature extractor and vectors combining multi-scale features for further classification. Experiments are conducted with Neural Network(NN)on ORL and Yale face datasets, and the experimental results suggest that multi-scale features are superior to single-scale ones on recognition rate and efficiency. Furthermore, fusion features of MSCAE and Histograms of Oriented Gradients(HOG)can get higher recognition rate than either of them.关键词
非监督特征学习/多尺度/卷积自动编码器/深度学习Key words
unsupervised feature learning/multi-scale/convolutional auto-encoder/deep learning分类
计算机与自动化引用本文复制引用
尹晓燕,冯志勇,徐超..多尺度非监督特征学习的人脸识别[J].计算机工程与应用,2016,52(14):136-141,196,7.基金项目
国家自然科学基金（No.61304262）。 （）
