基于MTCNN算法的多人脸识别研究.pdf
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1、PRINTING AND DIGITAL MEDIA TECHNOLOGY STUDY Tol.229 No.2 2024.04印刷与数字媒体技术研究 2024年第2期(总第229期)RESEARCH PAPERS研究论文Research on Multi-Face Recognition Based on MTCNN AlgorithmYANG Wen-peng1,SI Zhan-jun1,2*(1.College of Artificial Intelligence,Tianjin University of Science and Technology,Tianjin 300457,Ch
2、ina;2.College of Light Industry Science and Engineering,Tianjin University of Science and Technology,Tianjin 300457,China)Abstract With the rapid development of artificial intelligence in the field of computer vision,more and more classical artificial intelligence algorithms are applied to multiple
3、face recognition research.Among them,the MTCNN algorithm performs well in multi-face recognition,but there is still a relatively large space for improvement in recognition accuracy.In this study,based on the classical MTCNN algorithm framework,the refinement of its sub-algorithm NMS algorithm was ev
4、aluated and improved.The performance differences between the NMS algorithm and the improved NMS algorithm were compared and theoretically analyzed in each cascade network of P-Net,R-Net,and O-Net.The improved algorithm was evaluated and identified in multiple ways combining subjective and objective
5、and horizontal comparison and longitudinal comparison.The results showed that the model designed in this paper achieves a face recognition accuracy of 94.56%on the LFW dataset.It can provide a reference for multi-face recognition.Key words Multi-face recognition;MTCNN algorithm;Algorithm optimizatio
6、n基于MTCNN算法的多人脸识别研究杨文鹏1,司占军1,2*(1.天津科技大学 人工智能学院,天津 300457;2.天津科技大学 轻工科学与工程学院,天津 300457)摘要 随着人工智能在计算机视觉领域的飞速发展,越来越多的经典人工智能算法被应用于多人脸识别研究。其中,MTCNN算法在多人脸识别方面表现较好,但在识别精度上还有较大提升空间。本研究从经典的MTCNN算法框架出发,对其子算法NMS算法进行评估与改进,并对NMS算法与改进NMS算法在P-Net、R-Net、O-Net各个级联网络中的表现差异进行比较与理论分析。对改进后的NMS算法进行主观和客观相结合、横向比较与纵向比较相结合的多
7、种维度方式的评估与鉴别。实验结果表明,本研究设计的模型在数据集LFW上的人脸识别准确率为94.56%,可为多人脸识别研究提供参考。关键词 多人脸识别;MTCNN算法;算法优化中图分类号 TP39文献标识码 A文章编号 2097-2474(2024)02-116-07DOI 10.19370/10-1886/ts.2024.02.013收稿日期:2023-06-25 修回日期:2023-08-31 *为通讯作者本文引用格式:YANG Wen-peng,SI Zhan-jun.Research on Multi-Face Recognition Based on MTCNN Algorithm J
8、.Printing and Digital Media Technology Study,2024,(2):116-122.2024年2期印刷与数字媒体技术研究(拼版).indd 1162024年2期印刷与数字媒体技术研究(拼版).indd 1162024/4/26 17:08:082024/4/26 17:08:08117研究论文YANG Wen-peng et al:Research on Multi-Face Recognition Based on MTCNN Algorithm0 IntroductionFace recognition has been a direction of
9、 research in both academia and industry.Multi-face recognition faces more challenges,such as overlapping due to dense faces,variable size and resolution of multi-faces,and different poses of multi-faces 1-3.Rowley 4-5proposed a solution,which generated a binary classification model by training a neu
10、ral network to detect whether the image contains a face.Viola-Jones 6 proposed a component-based face recognition algorithm named DPM(Deformable Parts Model),which was outstanding in solving the task of faces with complex poses.Influenced by this,subsequent works 7-8 focused on combining multiple mo
11、dels to obtain diverse features to improve the performance of face recognition.However,these face recognition algorithms all train classifiers with the help of a set of manually labeled features,relying on the local feature extraction of the face,so they still cannot deal with multi-face recognition
12、 in complex scenes.In recent years,deep learning has been widely used in face recognition,due to its superior performance.Yan 9 proposed AlexNet as a five-layer convolutional,three-layer fully-connected network,which achieved good recognition results in related competitions.Krizhevsky 10-11 based on
13、 the Viola-Jones method proposed a idea of cascading to train CNNs,which made the overall model have the strong discriminative ability and recognition performance.Subsequently,more efficient target recognition networks R-CNN 12 series were proposed,including Fast R-CNN 13 and Faster R-CNN 6,which fu
14、rther improved the target recognition performance by extracting the feature vectors of candidate regions through the CNN network.With the public availability of the WIDER FACE dataset,a large number of multi-face scenarios appear,and conventional target recognition networks often fail to achieve goo
15、d recognition results.Therefore,more networks optimized for face recognition networks have been proposed,focusing on the problems of small-sized faces and multi-angle faces brought by multi-face scenarios.Girshick R et al 14 proposed Densebox,which cleverly used the full convolutional network,obtain
16、ed the results of predicting the target position coordinates and the target category simultaneously,and employed a multi-scale fusion strategy to provide a good recognition effect for small-sized faces.MTCNN(Multi-task Convolutional Neural Network)is a face recognition algorithm that utilizes a mult
17、i-task convolutional neural network.It works by using a cascade approach with a range of image pyramids to detect faces of varying sizes.It also incorporates three sub-networks to form a deep convolutional network that predicts faces and the locations of their features from coarse to fine.MTCNN algo
18、rithm is a popular face detection and alignment algorithm known for its high accuracy and efficiency.But,MTCNN performance may degrade when dealing with low-resolution or noisy images,as well as in challenging lighting conditions or occlusions.The algorithm may struggle to accurately detect faces in
19、 such scenarios.Also,MTCNN may produce false positive detections,where non-face regions are incorrectly identified as faces.This can impact the overall accuracy of the algorithm.In this study,aiming at the shortcomings of MTCNN,some algorithms within MTCNN were optimized for overall enhancement,and
20、the rationality and effectiveness of the optimization were demonstrated through experiments.The algorithm of this study can provide a reference for multi-face recognition.1 Research Method1.1 MTCNN AlgorithmMTCNN is a multi-task neural network model for 2024年2期印刷与数字媒体技术研究(拼版).indd 1172024年2期印刷与数字媒体技
21、术研究(拼版).indd 1172024/4/26 17:08:082024/4/26 17:08:08118印刷与数字媒体技术研究2024年第2期(总第229期)face recognition tasks proposed by the Shenzhen Research Institute of Chinese Academy of Sciences in 2016,which mainly employs three cascaded networks for fast and efficient face recognition using the idea of a candida
22、te box plus classifier.Its sub-algorithm is NMS(Non-Maximum Suppression).These three cascaded networks are P-Net for fast candidate window generation,R-Net for high-precision candidate window filter selection,and O-Net for generating the final bounding box with the key points of the face.The model a
23、lso uses techniques such as image pyramid,border regression,and non-maximal suppression for dealing with image problems.The technology roadmap in this study was shown in Fig.1.ImagepyramidNormaliz-ationPhotoswithfacesPhotos with facesand locationsSoft-NMSP-netSoft-NMSImproved-MTCNNR-netSoft-NMSO-net
24、Fig.1 Technology flow chart图1 技术流程图The full name of P-Net is Proposal Network and its basic construction is a fully convolutional network.For the image pyramid constructed in the previous step(images in MTCNN should all be normalized first with image pyramid operations),the FCN(Fully Convolutional N
25、etworks)was used for preliminary feature extraction and border calibration,and the Bounding-Box Regression was used to adjust the window and NMS was used to filter most of the windows.P-Net was a region proposal network for face region,which used a face classifier to determine whether the region was
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