多尺度非监督特征学习的人脸识别

Multi-scale unsupervised feature learning for face recognition

为了充分利用人脸图像的潜在信息,提出一种通过设置不同尺寸的卷积核来得到图像多尺度特征的方法,多尺度卷积自动编码器(Multi-Scale Convolutional Auto-Encoder,MSCAE)。该结构所提取的不同尺度特征反映人脸的本质信息,可以更好地还原人脸图像。这种特征提取框架是一个卷积和采样交替的层级结构,使得特征对旋转、平移、比例缩放等具有高度不变性。MSCAE以encoder-decoder模式训练得到特征提取器,用它提取特征,并融合形成用于分类的特征向量。BP神经网络在ORL和Yale人脸库上的分类结果表明,多尺度特征在识别率和性能上均优于单尺度特征。此外,MSCAE特征与HOG(Histograms of Oriented Gradients)的融合特征取得了比单一特征更高的识别率。

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.