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基于小人脸识别的高校课堂考勤系统研究 被引量:2
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作者 董亚蕾 张师宁 武旭聪 《现代信息科技》 2023年第12期62-65,共4页
针对小人脸检测容易出现漏检的问题,对YOLOv5算法进行改进,在YOLOv5的骨干网络中添加通道注意力模块,改进后的算法在Wider Face数据集上的测试识别准确率提高了3%;改进SRGAN算法,用残差密集网络替代生成网络,改进后的算法在LFW数据集上... 针对小人脸检测容易出现漏检的问题,对YOLOv5算法进行改进,在YOLOv5的骨干网络中添加通道注意力模块,改进后的算法在Wider Face数据集上的测试识别准确率提高了3%;改进SRGAN算法,用残差密集网络替代生成网络,改进后的算法在LFW数据集上测试,图像质量评价指标PSNR值达到31.5;最后采用FaceNet算法识别人脸。整合以上算法,完成高校课堂考勤系统开发。系统能够对课堂上学生的照片、视频进行人脸识别,并将识别的结果保存到数据库中供用户查看。 展开更多
关键词 课堂考勤 小人脸检测 超分辨率重建 人脸识别
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一种用于小人脸精准检测的图像超分辨率算法
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作者 俞文静 张明军 +1 位作者 李梓瑞 赖冬宜 《现代计算机》 2021年第1期68-72,共5页
针对实际应用中视频流的小人脸检测难度大、误检率高的问题,结合人脸图像结构信息提取,提出基于图像超分辨率的小人脸检测模型,设计一种应用于小人脸精准检测的改进POCS图像重建算法,该算法首先对小人脸图像进行图像增强(直方图均衡化... 针对实际应用中视频流的小人脸检测难度大、误检率高的问题,结合人脸图像结构信息提取,提出基于图像超分辨率的小人脸检测模型,设计一种应用于小人脸精准检测的改进POCS图像重建算法,该算法首先对小人脸图像进行图像增强(直方图均衡化、图像降噪、边缘增强),以获取小人脸结构信息,再将增强后的图像作为POCS超分辨率算法的基础帧,并在传统POCS算法中利用三次样条插值和中值滤波进行边缘优化超分辨率重建,并采用最小绝对差分配准则的块匹配算法进行图像配准和图像矫正,最后将三次插值、基本POCS小人脸图像以及本文提出的改进超分辨率图像输入到Dlib人脸检测模型中进行测试,实验结果表明本文模型的可行性和所提出的超分辨算法的有效性。 展开更多
关键词 小人脸检测 图像超分辨率 算法 凸集投影法
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一种融合上下文信息特征的改进MTCNN人脸检测算法 被引量:7
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作者 顾梅花 冯婧 杨娜 《西安工程大学学报》 CAS 2021年第6期114-120,共7页
在课堂场景下,针对多任务卷积神经网络(multi-task convolutional neural network,MTCNN)人脸检测算法对小人脸检测率较低的问题,提出一种改进的MTCNN算法。首先,对MTCNN算法网络模型的R-Net层网络集成上下文信息卷积模块,扩大特征图感... 在课堂场景下,针对多任务卷积神经网络(multi-task convolutional neural network,MTCNN)人脸检测算法对小人脸检测率较低的问题,提出一种改进的MTCNN算法。首先,对MTCNN算法网络模型的R-Net层网络集成上下文信息卷积模块,扩大特征图感受野获取更多小人脸信息;其次,引入反卷积层与最大池化层,以解决特征融合数据维度不一致问题;最后,对MTCNN算法网络模型的O-Net层网络集成上下文信息卷积模块,进一步提取小人脸特征信息,并引入2个卷积池化层进行特征融合。实验结果表明:与MTCNN算法相比,所提方法在FDDB数据集上检测精度提升3%,在课堂场景数据集上人脸检测召回率与F_(1)分数分别提升8.69%和4.94%。 展开更多
关键词 上下文信息 特征融合 多任务卷积神经网络 人脸检测 课堂场景 小人脸
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Enhanced kernel minimum squared error algorithm and its application in face recognition
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作者 赵英男 何祥健 +1 位作者 陈北京 赵晓平 《Journal of Southeast University(English Edition)》 EI CAS 2016年第1期35-38,共4页
To improve the classification performance of the kernel minimum squared error( KMSE), an enhanced KMSE algorithm( EKMSE) is proposed. It redefines the regular objective function by introducing a novel class label ... To improve the classification performance of the kernel minimum squared error( KMSE), an enhanced KMSE algorithm( EKMSE) is proposed. It redefines the regular objective function by introducing a novel class label definition, and the relative class label matrix can be adaptively adjusted to the kernel matrix.Compared with the common methods, the newobjective function can enlarge the distance between different classes, which therefore yields better recognition rates. In addition, an iteration parameter searching technique is adopted to improve the computational efficiency. The extensive experiments on FERET and GT face databases illustrate the feasibility and efficiency of the proposed EKMSE. It outperforms the original MSE, KMSE,some KMSE improvement methods, and even the sparse representation-based techniques in face recognition, such as collaborate representation classification( CRC). 展开更多
关键词 minimum squared error kernel minimum squared error pattern recognition face recognition
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LOCAL BAGGING AND ITS APPLICATIONON FACE RECOGNITION 被引量:1
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作者 朱玉莲 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI 2010年第3期255-260,共6页
Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample si... Bagging is not quite suitable for stable classifiers such as nearest neighbor classifiers due to the lack of diversity and it is difficult to be directly applied to face recognition as well due to the small sample size (SSS) property of face recognition. To solve the two problems,local Bagging (L-Bagging) is proposed to simultaneously make Bagging apply to both nearest neighbor classifiers and face recognition. The major difference between L-Bagging and Bagging is that L-Bagging performs the bootstrap sampling on each local region partitioned from the original face image rather than the whole face image. Since the dimensionality of local region is usually far less than the number of samples and the component classifiers are constructed just in different local regions,L-Bagging deals with SSS problem and generates more diverse component classifiers. Experimental results on four standard face image databases (AR,Yale,ORL and Yale B) indicate that the proposed L-Bagging method is effective and robust to illumination,occlusion and slight pose variation. 展开更多
关键词 face recognition local Bagging (L-Bagging) small sample size (SSS) nearest neighbor classifiers
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Face recognition by combining eigenface method with different wavelet subbands
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作者 MA Yan LI Shun-bao 《Optoelectronics Letters》 EI 2006年第5期383-385,共3页
A method combining eigenface with different wavelet subbands for face recognition is proposed.Each training image is decomposed into multi-subbands for extracting their eigenvector sets and projection vectors.In the r... A method combining eigenface with different wavelet subbands for face recognition is proposed.Each training image is decomposed into multi-subbands for extracting their eigenvector sets and projection vectors.In the recognition process,the inner product distance between the projection vectors of the test image and that of the training image are calculated.The training image,corresponding to the maximum distance under the given threshold condition,is considered as the final result.The experimental results on the ORL and YALE face database show that,compared with the eigenface method directly on the image domain or on a single wavelet subband,the recognition accuracy using the proposed method is improved by 5% without influencing the recognition speed. 展开更多
关键词 人脸识别 小波 微分变换 图象处理
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Face Recognition Using LDA with Wavelet Transform Approach
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作者 Neeta Nain Akshay Kumar +3 位作者 Amlesh Kumar Mohapatra Ashok Kumar Ratan Das Nemi Chand Singh 《Computer Technology and Application》 2011年第5期401-405,共5页
Linear Discriminant Analysis (LDA) is one of the principal techniques used in face recognition systems. LDA is well-known scheme for feature extraction and dimension reduction. It provides improved performance over ... Linear Discriminant Analysis (LDA) is one of the principal techniques used in face recognition systems. LDA is well-known scheme for feature extraction and dimension reduction. It provides improved performance over the standard Principal Component Analysis (PCA) method of face recognition by introducing the concept of classes and distance between classes. This paper provides an overview of PCA, the various variants of LDA and their basic drawbacks. The paper also has proposed a development over classical LDA, i.e., LDA using wavelets transform approach that enhances performance as regards accuracy and time complexity. Experiments on ORL face database clearly demonstrate this and the graphical comparison of the algorithms clearly showcases the improved recognition rate in case of the proposed algorithm. 展开更多
关键词 Face recognition principal component analysis (PCA) linear discriminant analysis (LDA) relevance weighted LDA (RW-LDA) LDA/QR wavelet transform sub-bands.
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