人脸辅助诊断关键技术研究
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摘要
某些疾病的筛查和诊断可以借助患者的面部变化来进行辅助性判断。目前,人脸辅助诊断技术在医学辅助诊断领域的应用不断增加,其在某些疾病中的表现已经不低于医学工作者的经验性诊断。人脸辅助诊断系统可以帮助对疾病进行大规模筛查,判断疾病分期,最终缩短确诊时间,提高医生工作效率,造福患者。回顾了人脸检测、人脸特征提取、人脸关键点识别、人脸矫正、人脸图像增强、分类器选择等人脸辅助诊断关键技术,展望了人脸辅助诊断的发展前景。
Certain diagnosis and screening of disease can make auxiliary judgements with the help of the patient's facial changes. At present, the application of face aided diagnosis technology in the field of medical auxiliary diagnosis is increasing.The performance of some diseases is no less than the empirical diagnosis of medical workers. Face assisted diagnosis systems can help to carry out large-scale screening of disease, accelerate disease staging, and shorten diagnosis time,improve the efficiency of doctors, for the benifit of patients. This paper reviews the key of facial-assisted diagnosis, such as face detection, face feature extraction, face landmark recognition, face correction, face image enhancement, classifier selection, and look forward to the development prospects of face aided diagnosis.
Certain diagnosis and screening of disease can make auxiliary judgements with the help of the patient's facial changes. At present, the application of face aided diagnosis technology in the field of medical auxiliary diagnosis is increasing.The performance of some diseases is no less than the empirical diagnosis of medical workers. Face assisted diagnosis systems can help to carry out large-scale screening of disease, accelerate disease staging, and shorten diagnosis time,improve the efficiency of doctors, for the benifit of patients. This paper reviews the key of facial-assisted diagnosis, such as face detection, face feature extraction, face landmark recognition, face correction, face image enhancement, classifier selection, and look forward to the development prospects of face aided diagnosis.
引文
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[29]雷毅.针对特纳综合征的自动人脸分类方法研究[D].太原:中北大学,2018.
[30] Zhao Q,Okada K,Rosenbaum K,et al.Digital facial dysmorphology for genetic screening:hierarchical constrained local model using ICA[J].Medical Image Analysis,2014,18(5):699-710.
[31] Josanne C B,Anouk V,Carla H,et al.Three-dimensional face shape in fabry disease[J].European Journal of Human Genetics,2007,15(5):535-542.
[32] Cerrolaza J J,Porras A R,Mansoor A,et al.Identification of dysmorphic syndromes using landmark-specific local texture descriptors[C]//2016 IEEE 13th International Symposium on Biomedical Imaging(ISBI).Prague,Czech Republic:IEEE,2016:1080-1083.
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[34] Le T H,Bui L.Face recognition based on SVM and2DPCA[J].International Journal of Signal Processing,Image Processing and Pattern Recognition,2011,4(3):85-93.
[35]袁程波.基于PCA和SVM的人脸识别关键技术研究与实现[D].成都:电子科技大学,2017.
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[37] Cerrolaza J J,Porras A R,Mansoor A,et al.Identification of dysmorphic syndromes using landmark-specific local texture descriptors[C]//IEEE International Symposium on Biomedical Imaging,2016.
[38]?zdemir M E,Telatar Z,Ero?ul O,et al.Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree[J].Australasian Physical Engineering Science in Medicine,2018,41(2):451-461.
[39] Gurovich Y,Hanani Y,Bar O,et al.Deepgestalt-identifying rare genetic syndromes using deep learning[J].ar Xiv:1801.07637,2018.
[40]李海彦,徐汀荣,张立晓,等.基于仿射变换的多姿态人脸矫正和识别[J].计算机应用研究,2014,31(4):1215-1219.
[41] Taigman Y,Yang M,Mar C,et al.Deep Face:closing the gap to human-level performance in face verification[C]//IEEE Conference on Computer Vision&Pattern Recognition,2014.
[42]梁琳,何卫平,雷蕾,等.光照不均图像增强方法综述[J].计算机应用研究,2010,27(5):1625-1628.
[43]盛道清.图像增强算法的研究[D].武汉:武汉科技大学,2007.
[44]王彦林,张进.改进成像模型的单尺度Retinex彩色图像增强算法[J].计算机工程与设计,2018,39(11):3511-3515.
[2] Chen Y,Liu W,Zhang L,et al.Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis[J].Computers in Biology and Medicine,2015,64:30-39.
[3] Kosilek R,Schopohl J,Grunke M,et al.Automatic face classification of cushing’s syndrome in women-a novel screening approach[J].Experimental and Clinical Endocrinology&Diabetes,2013,121(9):561-564.
[4] Schneider H J,Kosilek R P,GüNther M,et al.A novel approach to the detection of acromegaly:accuracy of diagnosis by automatic face classification[J].The Journal of Clinical Endocrinology&Metabolism,2011,96(7):2074-2080.
[5]李娥.人脸检测方法综述[J].信息技术与信息化,2018(4):24-26.
[6] Viola P,Jones M.Rapid object detection using a boosted cascade of simple features[C]//Proceedings of the 2001IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2001.Kauai,HI,USA:IEEE Comput Soc,2001:511-518.
[7]魏喆.基于AdaBoost的人脸检测[D].北京:北京邮电大学,2012.
[8]钟汉亭,姚晓东.一种改进的强分类器优化算法[J].计算机应用,2009,29(S2):267-268.
[9] Yang S,Luo P,Loy C C,et al.Faceness-net:face detection through deep facial part responses[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2017(99):1.
[10] Li H,Lin Z,Shen X,et al.A convolutional neural network cascade for face detection[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition.Boston,MA,USA:IEEE,2015:5325-5334.
[11] Kong X,Gong S,Su L,et al.Automatic detection of acromegaly from facial photographs using machine learning methods[J].Ebiomedicine,2018,27(C):94-102.
[12] Song W,Lei Y,Chen S,et al.Multiple facial image features-based recognition for the automatic diagnosis of turner syndrome[J].Computers in Industry,2018,100:85-95.
[13] Cootes T F,Taylor C J,Cooper D H,et al.Active shape models-their training and application[J].Computer Vision and Image Understanding,1995,61(1):38-59.
[14] Edwards G J,Cootes T F,Taylor C J.Face recognition using active appearance models[C]//Burkhardt H,Neumann B.European Conference on Computer Vision(ECCV’98).Berlin,Heidelberg:Springer,1998:581-595.
[15] Dollar P,Welinder P,Perona P.Cascaded pose regression[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.San Francisco,CA,USA:IEEE,2010:1078-1085.
[16] Kazemi V,Sullivan J.One millisecond face alignment with an ensemble of regression trees[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus,OH:IEEE,2014:1867-1874.
[17] Sun Y,Wang X,Tang X.Deep convolutional network cascade for facial point detection[C]//2013 IEEE Conference on Computer Vision and Pattern Recognition.Portland,OR,USA:IEEE,2013:3476-3483.
[18] Zhang K,Zhang Z,Li Z,et al.Joint face detection and alignment using multitask cascaded convolutional networks[J].IEEE Signal Processing Letters,2016,23(10):1499-1503.
[19] Wu Y,Hassner T.Facial landmark detection with tweaked convolutional neural networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,40(12):3067-3074.
[20] Kowalski M,Naruniec J,Trzcinski T.Deep alignment network:a convolutional neural network for robust face alignment[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops.Honolulu,HI,USA:IEEE,2017:2034-2043.
[21]陈梦竹.基于肤色检测的中医面色识别[D].北京:北京交通大学,2018.
[22]周琳琳,何中市.基于Fisherface和SIFT特征集成的人脸识别[J].现代计算机,2017(2):58-62.
[23] Park J K,Park H H,Park J.Distributed eigenfaces for massive face image data[J].Multimedia Tools and Applications,2017,76(24):25983-26000.
[24] Taigman Y,Yang M,Ranzato M,et al.Deepface:closing the gap to human-level performance in face verification[C]//2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus,OH,USA:IEEE,2014:1701-1708.
[25] Sun Y,Wang X,Tang X.Deep learning face representation by joint identification verification[J].ar Xiv.org:1406.4773,2014.
[26] Yi D,Lei Z,Liao S,et al.Learning face representation from scratch[J].arXiv:1411.7923v1,2014.
[27] Simonyan K,Zisserman A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556v6,2104.
[28] Chen S,Liu Y,Gao X,et al.Mobile FaceNets:efficient CNNS for accurate real-time face verification on mobile devices[C]//Chinese Conference on Biometric Recognition,2018:428-438.
[29]雷毅.针对特纳综合征的自动人脸分类方法研究[D].太原:中北大学,2018.
[30] Zhao Q,Okada K,Rosenbaum K,et al.Digital facial dysmorphology for genetic screening:hierarchical constrained local model using ICA[J].Medical Image Analysis,2014,18(5):699-710.
[31] Josanne C B,Anouk V,Carla H,et al.Three-dimensional face shape in fabry disease[J].European Journal of Human Genetics,2007,15(5):535-542.
[32] Cerrolaza J J,Porras A R,Mansoor A,et al.Identification of dysmorphic syndromes using landmark-specific local texture descriptors[C]//2016 IEEE 13th International Symposium on Biomedical Imaging(ISBI).Prague,Czech Republic:IEEE,2016:1080-1083.
[33]王宏漫,欧宗瑛.采用PCA/ICA特征和SVM分类的人脸识别[J].计算机辅助设计与图形学学报,2003,15(4):416-420.
[34] Le T H,Bui L.Face recognition based on SVM and2DPCA[J].International Journal of Signal Processing,Image Processing and Pattern Recognition,2011,4(3):85-93.
[35]袁程波.基于PCA和SVM的人脸识别关键技术研究与实现[D].成都:电子科技大学,2017.
[36] Huang C,Bo H.Study on face recognition based on CNN and SVM[J].Microcomputer&Its Applications,2017(15).
[37] Cerrolaza J J,Porras A R,Mansoor A,et al.Identification of dysmorphic syndromes using landmark-specific local texture descriptors[C]//IEEE International Symposium on Biomedical Imaging,2016.
[38]?zdemir M E,Telatar Z,Ero?ul O,et al.Classifying dysmorphic syndromes by using artificial neural network based hierarchical decision tree[J].Australasian Physical Engineering Science in Medicine,2018,41(2):451-461.
[39] Gurovich Y,Hanani Y,Bar O,et al.Deepgestalt-identifying rare genetic syndromes using deep learning[J].ar Xiv:1801.07637,2018.
[40]李海彦,徐汀荣,张立晓,等.基于仿射变换的多姿态人脸矫正和识别[J].计算机应用研究,2014,31(4):1215-1219.
[41] Taigman Y,Yang M,Mar C,et al.Deep Face:closing the gap to human-level performance in face verification[C]//IEEE Conference on Computer Vision&Pattern Recognition,2014.
[42]梁琳,何卫平,雷蕾,等.光照不均图像增强方法综述[J].计算机应用研究,2010,27(5):1625-1628.
[43]盛道清.图像增强算法的研究[D].武汉:武汉科技大学,2007.
[44]王彦林,张进.改进成像模型的单尺度Retinex彩色图像增强算法[J].计算机工程与设计,2018,39(11):3511-3515.