结合CBAM和Siamese神经网络的人脸识别算法

Q3 Materials Science
Xiangzhou MENG, Yingjun LI, Guicong WANG, Tiansheng MENG
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引用次数: 0

摘要

我是一名学生,我想在这里和大家分享一下我的故事。暹罗神经网络(Siamese neural network)网络VGG11CBAM的 "FaceV5 "功能FaceV5 FaceV5 FaceV5 FaceV5 FaceV5 FaceV567%CAS-PEAL-R1RGESVGG11+siamese6.05%6.7%ã该ç®ç®æ³å¯ä"¥å¨¨å¤å´ å½±åä¸æ´å¥å°½è¿è¿è¡è¡äº¸¸è¯å "é "ªè¯¼å -æ该稳å®æ§ã
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Face recognition algorithm incorporating CBAM and Siamese neural network
é’ˆå¯¹ä¼ ç»Ÿäººè„¸è¯†åˆ«æ–¹æ³•è¯†åˆ«æ€§èƒ½è¾ƒå·®ï¼ŒåŸºäºŽæ·±åº¦å­¦ä¹ çš„æ–¹æ³•åœ¨éžé™åˆ¶æ¡ä»¶ä¸‹è¯†åˆ«è¾ƒä¸ºå›°éš¾ï¼Œäººè„¸ç‰¹å¾åŒºåˆ†æ€§å¼±ï¼Œè¯†åˆ«ç²¾åº¦å®¹æ˜“å—åˆ°å§¿åŠ¿ã€è¡¨æƒ ç­‰æ–¹é¢å½±å“çš„é—®é¢˜ï¼Œæå‡ºäº†ä¸€ç§å¼•å ¥å·ç§¯å—æ³¨æ„åŠ›æ¨¡å—çš„å­ªç”Ÿç¥žç»ç½‘ç»œæ¨¡åž‹ç»“æž„ã€‚è¯¥ç»“æž„æ˜¯åŸºäºŽå­ªç”Ÿç¥žç»ç½‘ç»œï¼ˆSiamese neural networkï¼‰çš„åŸºç¡€æ¡†æž¶è¿›è¡Œæ”¹è¿›çš„ï¼Œåœ¨æ¡†æž¶ä¸­å¼•å ¥æ”¹è¿›çš„VGG11_BN模型进行特征提取。该模型是在VGG11ç»“æž„çš„åŸºç¡€ä¸Šå¼•å ¥æ‰¹å½’ä¸€åŒ–ï¼ˆBatch Normalization,BNï¼‰æŠ€æœ¯ï¼Œåœ¨åŽŸæ¨¡åž‹ç»“æž„çš„åŸºç¡€ä¸Šï¼Œæå‡ºå¼•å ¥CBAMæ··åˆæ³¨æ„åŠ›æœºåˆ¶çš„ç‰¹å¾æå–ç½‘ç»œï¼›æœ€åŽï¼Œé’ˆå¯¹ç›®å‰äºšæ´²äººçš„äººè„¸è¯†åˆ«è®­ç»ƒè¾ƒå°‘çš„æƒ å†µï¼Œé‡‡ç”¨æ›´åŠ ç¬¦åˆäºšæ´²äººè„¸ç‰¹å¾çš„CASIA-FaceV5数据集进行识别训练。实验结果表明:本文算法在人脸识别方面的准确率达到了96.67%,并且在CAS-PEAL-R1人脸数据集上比SRGES,VGG11+siamese算法的准确率分别提升6.05%,6.7%ã€‚è¯¥ç®—æ³•å¯ä»¥åœ¨å¤šå› ç´ å½±å“ä¸‹æ›´å¥½åœ°è¿›è¡Œäººè„¸è¯†åˆ«éªŒè¯ï¼Œå ·æœ‰è‰¯å¥½çš„ç¨³å®šæ€§ã€‚
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来源期刊
Guangxue Jingmi Gongcheng/Optics and Precision Engineering
Guangxue Jingmi Gongcheng/Optics and Precision Engineering Materials Science-Electronic, Optical and Magnetic Materials
CiteScore
2.40
自引率
0.00%
发文量
95
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