基于云计算的智能人脸识别借阅系统研究
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摘要
针对目前大多数高校图书馆仍旧使用借阅卡、一卡通等传统借阅系统的现状,提出了构建基于云计算的智能人脸识别借阅系统。相比于在本地设备完成计算的人脸识别系统运算慢、存储资源有限等缺点,系统采用具有高性能计算能力和海量存储能力的云平台。通过采集人脸数据,将收集到的人脸信息上传到云平台,云平台采用二值化、光线补偿、灰度变化、对比度增强等方法进行处理后对人脸特征信息进行保存并备份。当扫描待测人脸时,将人脸信息上传至云端与备份信息进行对比识别。测试结果表明;基于云计算的智能人脸识别借阅系统具有较好的准确性、实时性、稳定性,降低了本地存储负担和成本、较好的满足了用户的智能化借阅需求。
Because most university libraries still use traditional lending system ssuch as readers' card and all in one-card,an intelligent face recognition lending system based on cloud computing is proposed. Compared with the face recognition system that finishes by computing in local devices,which has the shortcomings of slow operation and limited storage resources,this system adopts a cloud platform with high performance computing power and massive storage capacity. Uploads the collected face information to the cloud platform by collecting face data,and the cloud platform uses binarization,light compensation,gray change,contrast enhancement and other methods to save and back up the face features information. When scanning the face is tested,the face information is uploaded to the cloud and compared with the backup information for identification. The test results show that the intelligent face recognition lending system based on cloud computing has better accuracy,real-time and stability,which reduces the burden and cost of local storage,and it can better meet a need of user's intelligent lending.
Because most university libraries still use traditional lending system ssuch as readers' card and all in one-card,an intelligent face recognition lending system based on cloud computing is proposed. Compared with the face recognition system that finishes by computing in local devices,which has the shortcomings of slow operation and limited storage resources,this system adopts a cloud platform with high performance computing power and massive storage capacity. Uploads the collected face information to the cloud platform by collecting face data,and the cloud platform uses binarization,light compensation,gray change,contrast enhancement and other methods to save and back up the face features information. When scanning the face is tested,the face information is uploaded to the cloud and compared with the backup information for identification. The test results show that the intelligent face recognition lending system based on cloud computing has better accuracy,real-time and stability,which reduces the burden and cost of local storage,and it can better meet a need of user's intelligent lending.
引文
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[15]陆娇蓝,陈军,杨著.基于云计算的嵌入式人脸识别系统建构与研究[J].计算机测量与控制,2016,24(4):146-148.LU Jiaolan,CHEN Jun,YANG Zhu. Construction and Research of Embedded Face Recognition System Based on Cloud Computing[J]. Computer Measurement&Control,2016,24(4):146-148.
[16]韩东,王学军.基于改进的卷积神经网络多姿态人脸识别研究[J].吉林大学学报:信息科学版,2018,36(5):376-381.HAN Dong,WANG Xuejun. Research on Multi-Pose Face Recognition Based on Improved Convolutional Neural Network[J].Journal of Jilin University:Information Science Edition,2018,36(5):376-381.
[2]杜红军,李巍,赵永彬,等.基于Open Stack体系的云计算基础架构研究[J].吉林大学学报:信息科学版,2018,36(2):213-217.DU Hongjun,LI Wei,ZHAO Yongbin,et al. Research on Cloud Computing Infrastructure Based on Open Stack System[J].Journal of Jilin University:Information Science Edition,2018,36(2):213-217.
[3]张泊平.云计算平台下的人脸识别[J].现代电子技术,2016,39(18):88-90,95.ZHANG Boping. Face Recognition under Cloud Computing Platform[J]. Modern Electronic Technique, 2016,39(18):88-90,95.
[4]王维秋,刘春丽.基于人脸识别技术的我国图书馆智慧服务功能设计与模式构建[J].图书馆学研究,2018(18):44-50.WANG Weiqiu,LIU Chunli,Intelligent Service Function Design and Pattern Construction of Chinese Libraries Based on Face Recognition Technology[J]. Library Science Research,2018(18):44-50.
[5]邱爽,聂仁灿,周冬明,等.自归一化卷积神经网络的人脸识别方法[J].云南大学学报:自然科学版,2018,40(4):659-664.QIU Shuang,NIE Rencan,ZHOU Dongming,et al. Face Recognition Method Based on Self-Normalized Convolution Neural Network[J]. Journal of Yunnan University:Natural Science Edition,2018,40(4):659-664.
[6]GUO Cheng,TANG Xinyu,JIE Yingmo,FENG Bin. Efficient Method to Verify the Integrity of Data with Supporting Dynamic Data in Cloud Computing[J]. Science China:Information Sciences,2018,61(11):235-237.
[7]WU Yulin,JIANG Mingyan. Face Recognition System Based on CNN and LBP Features for Classifier Optimization and Fusion[J]. The Journal of China Universities of Posts and Telecommunications,2018,25(1):37-47.
[8]宋凌怡.基于肤色模型的人脸检测与人眼定位[J].吉林大学学报:理学版,2015,53(6):1246-1250.SONG Lingyi. Face Detection and Human Eye Location Based on Skin Color Model[J]. Journal of Jilin University:Science Edition,2015,53(6):1246-1250.
[9]王银花,王丽萍,王忠良.基于判别分析与低秩投影的人脸识别算法[J].吉林大学学报:理学版,2018,56(2):355-360.WANG Yinhua,WANG Liping,WANG Zhongliang. Face Recognition Algorithm Based on Discriminant Analysis and Low Rank Projection[J]. Journal of Jilin University:Science Edition,2018,56(2):355-360.
[10]杨巨成,李琼,刘娜,等.基于局部边缘差异二值模式的人脸识别方法[J].天津科技大学学报,2016,31(4):69-73.YANG Jucheng,LI Qiong,LIU Na,et al. Face Recognition Method Based on Local Edge Difference Binary Pattern[J].Journal of Tianjin University of Science and Technology,2016,31(4):69-73.
[11]欧阳宁,钟欢虹,袁华,等.针对光照变化的人脸识别算法研究[J].电子技术应用,2015,41(5):152-155.OUYANG Ning,ZHONG Huanhong,YUAN Hua,et al. Study on Face Recognition Algorithm for Illumination Change[J].Application of Electronic Technology,2015,41(5):152-155.
[12]李振东,钟勇,张博言,等.基于深度特征聚类的海量人脸图像检索[J].哈尔滨工业大学学报,2018,50(11):101-109.LI Zhendong,ZHONG Yong,ZHANG Boyan,et al. Massive Face Image Retrieval Based on Depth Feature Clustering[J].Journal of Harbin Institute of Technology,2018,50(11):101-109.
[13]范哲意,曾亚军,蒋姣,等.改进的稀疏表示静止-视频人脸识别算法[J].信号处理,2016,32(5):567-574.FAN Zheyi,ZENG Yajun,JIANG Jiao,et al. Improved Sparse Representation Static-Video Face Recognition Algorithms[J].Signal Processing,2016,32(5):567-574.
[14]钟锐,吴怀宇,何云.基于局部融合特征与分层增量树的快速人脸识别算法[J].计算机科学,2018,45(6):308-313.ZHONG Rui,WU Huaiyu,HE Yun. Fast Face Recognition Algorithms Based on Local Fusion Features and Hierarchical Incremental Tree[J]. Computer Science,2018,45(6):308-313.
[15]陆娇蓝,陈军,杨著.基于云计算的嵌入式人脸识别系统建构与研究[J].计算机测量与控制,2016,24(4):146-148.LU Jiaolan,CHEN Jun,YANG Zhu. Construction and Research of Embedded Face Recognition System Based on Cloud Computing[J]. Computer Measurement&Control,2016,24(4):146-148.
[16]韩东,王学军.基于改进的卷积神经网络多姿态人脸识别研究[J].吉林大学学报:信息科学版,2018,36(5):376-381.HAN Dong,WANG Xuejun. Research on Multi-Pose Face Recognition Based on Improved Convolutional Neural Network[J].Journal of Jilin University:Information Science Edition,2018,36(5):376-381.
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