基于极端学习机的人脸特征深度稀疏自编码方法  

Deep sparse auto-encoder method using extreme learning machine for facial features

作　　者：张欢欢[1] 洪敏 袁玉波 ZHANG Huanhuan;HONG Min;YUAN Yubo(College of Information Science and Engineering,East China University of Science and Technology,Shanghai 200237,China)

机构地区：[1]华东理工大学信息科学与工程学院,上海200237

出　　处：《计算机应用》2018年第11期3193-3198,共6页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61001200);上海市科研计划项目(17DZ1101003)~~

摘　　要：针对输入人脸特征的不准确性导致识别系统识别率不高的问题,提出了一种有效的基于极端学习机(ELM)的人脸特征深度稀疏自编码(DSAE)方法。首先,利用截断式核范数构造损失函数,通过最小化损失函数提取人脸图像的稀疏特征;其次,利用极端学习机自编码器(ELM-AE)模型进行人脸特征的自编码,实现数据维度的降低以及噪声过滤;最后,通过经验风险极小化得到最优的深度结构。在ORL、IMM、Yale和UMIST人脸数据集上的实验结果表明,DSAE方法对高维人脸图像的识别率明显优于极端学习机、随机森林(RF)等算法,且具有良好的泛化性能。Focused on the problem of low recognition in recognition systems caused by the inaccuracy of input features,an efficient Deep Sparse Auto-Encoder(DSAE)method using Extreme Learning Machine(ELM)for facial features was proposed.Firstly,truncated nuclear norm was used to construct loss function,and sparse features of face images were extracted by minimizing loss function.Secondly,self-encoding of facial features was used by Extreme Learning Machine Auto-Encoder(ELM-AE)model to achieve data dimension reduction and noise filtering.Thirdly,the optimal depth structure was obtained by minimizing the empirical risk.The experimental results on ORL,IMM,Yale and UMIST datasets show that the DSAE method not only has higher recognition rate than ELM,Random Forest(RF),etc.on high-dimensional face images,but also has good generalization performance.

关 键 词：人脸图像 极端学习机 自编码器 截断式核范数正则化 稀疏特征 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]