基于深度集成学习的人脸智能反馈认知方法  

Research on intelligent face cognition method with deep ensemble learning and feedback mechanism

作　　者：范叶平 李玉 杨德胜 万涛 马冬 李帷韬[2] Fan Yeping;Li Yu;Yang Desheng;Wan Tao;Ma Dong;Li Weitao(State Grid Communication Industry Group CO.,LTD.,Anhui Jiyuan Software CO.,LTD.,Hefei 230088,China;School of Electric Engineering and Automation,Hefei University of Technology,Hefei 230009,China)

机构地区：[1]国网信通产业集团安徽继远软件有限公司,安徽合肥230088 [2]合肥工业大学电气与自动化工程学院,安徽合肥230009

出　　处：《电子技术应用》2019年第5期5-8,13,共5页Application of Electronic Technique

基　　金：国家电网公司科技项目(52110418001L)

摘　　要：人脸识别技术是深度学习的重要研究领域。为了克服传统开环人脸认知模式以及深层神经网络结构的缺陷,模仿人类实时评测认知结果自寻优调节特征空间和分类认知准则的认知模式,借鉴闭环控制理论思想,探索了一种基于深度集成学习的人脸智能反馈认知方法。首先,基于DEEPID网络建立人脸图像由全局到局部具有确定映射关系的非结构化特征空间;其次,基于特征可分性评测和变精度粗糙集理论,从信息论角度建立非结构化动态特征表征的人脸认知决策信息系统模型,以约减非结构化特征空间;再次,采用集成随机权向量函数连接网络,构建简约非结构化特征空间的分类认知准则;最后,构建人脸认知结果熵测度指标,为人脸特征空间和分类认知准则的自寻优调节机制提供量化依据。实验结果表明,较已有方法,该方法有效地提高了人脸图像的识别率。Face recognition technology is an important research field for deep learning. In order to overcome the shortcomings of traditional open-loop face cognition mode and deep neural network structure, and to imitate human cognition model of real-time evaluation of cognitive results to self-optimized regulate feature space and classification cognition criteria, drawing on the theory of closed-loop control theory, this paper explores an intelligent face cognition method with deep ensemble learning and feedback mechanism. Firstly, based on the DEEPID neural network, an unstructured feature space of face images with a determined mapping relationship from the global to the local is established. Secondly, based on feature separability evaluation and variable precision rough set theory, a face cognition decision information system model with unstructured dynamic feature representation is established from the perspective of information theory, to reduce the unstructured feature space. Thirdly, the ensemble random vector functional-link net is used to construct the classification criterion of the reduced unstructured feature space. Finally, the face cognition result entropy measure index is constructed to provide a quantitative basis for the self-optimization adjustment mechanism of face feature space and classification cognition criteria. The experimental results show that the proposed method can effectively improve the recognition rate of face images compared with the existing methods.

关 键 词：人脸识别 智能反馈 集成分类器 熵测度 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]