关键词: 非监督特征学习, 多尺度, 卷积自动编码器, 深度学习

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.

Key words: unsupervised feature learning, multi-scale, convolutional auto-encoder, deep learning