中文摘要：

人脸识别的关键在于特征提取,过去主要从完美的低维特征子空间来刻画高维图像,但是近年来深度学习模型为特征提取提供新方向。本文提出在Gabor特征描述子调制下的深度子空间模型,在深度子空间这一新型深度学习框架基础上,使用Gabor滤波器组处理图像,并构建深度特征提取多层网络,得到Gabor调制下的深层抽象特征。首先将传统的8个方向5个尺度的40个Gabor滤波器在尺度上进行压缩得到8个基本Gabor滤波器组;然后将经过Gabor滤波的描述特征分别送入深度化改造的子空间模型,得到图像的深层特征表示;其次将这些特征进行哈希编码,直方图分块,作为描述特征。本文在FERET、ORL、CMU_PIE等数据库上讨论加入Gabor滤波器调制后的深度多层子空间特征提取模型在人脸识别问题上性能的提升,实验结果表明,该算法可以取得较好的识别率,并对光照、表情、姿态等有很好的鲁棒性,能够弥补浅层网络易受训练图像影响的缺点。

英文摘要：

The key point of face recognition is feature extraction,which was mainly described by a perfect lower dimensional feature space to image the high dimensional in the past,but recently,had been provided a new direction by the deep learning model. In this paper,a deep subspace model that under the Gabor feature descriptor modulation is put forward. The modulation based on a new type deep learning framework uses Gabor filter to process images,draws a multi-layer network from the deep feature extraction built,gets the deep abstract feature modulated by the Gabor. Firstly,we compress the traditional 40 Gabor filters of 8 directions and 5 scales in scale into 8 basic Gabor filter banks; Then we put the description features filtered by Gabor into the deeply reformed subspace model separately to get the deep feature representation of the image; Secondly,deal these features with hash code,histogram block as the description features. We discuss the improvement of the face recognition by adding the deep multi-layer subspace feature extraction modulation based on the FERET,ORL,CMU_ PIE database. The results show that this algorithm could make a better discrimination,good robustness on the illumination,expression,and posture. Meanwhile,it also makes up for the shortcoming of the shallow layer network who easily affected by the training image.