摘要
针对人脸姿态分类问题,本文提出了一种基于深度学习与融入梯度信息的人脸姿态分类学习方法。首先提取人脸姿态图像灰度与灰度差组合特征,然后通过三层受限玻尔兹曼机(Restricted Boltzmann machines,RBM)对大量样本的特征进行融合训练学习,提取反映人脸姿态内涵的深度学习特征。最后通过Softmax分类器建立深度学习特征与人脸姿态标签的对应关系。在对CAS-PEAL-R1人脸数据库进行学习和分类检测中,获得普遍高于95%的分类精度。
Aiming at upgrading the performance of face pose classification,we proposed an algorithm of face pose classification based on deep learning and gradient information fusion.First,the pixel gray intensity features and the features of gray intensity difference nearby each pixel from a face image are extracted.Then,these features of face images are processed with deep learning technique through a dedicated three-layer restricted Boltzmann machines network,which has been trained by a large number of samples.Finally,a corresponding relation between fusion deep learning features and the labels of face pose classifications is built through a Softmax classifier.The experiment results show that the proposed algorithm achieves a state of the art classification accuracy,generally higher than 95%,when learning and testing on CAS-PEAL-R1 face database.
出处
《数据采集与处理》
CSCD
北大核心
2016年第5期941-948,共8页
Journal of Data Acquisition and Processing
基金
大连理工大学科研专项基金(822030)资助项目
关键词
人脸姿态分类
深度学习
受限玻尔兹曼机
face pose classification
deep learning
restricted Boltzmann machines
