
JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE) ›› 2022, Vol. 42 ›› Issue (1): 124-129.doi: 10.3969/j.issn.1674-8115.2022.01.019
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Received:2021-07-21
Online:2022-01-28
Published:2022-01-28
Contact:
Qing FAN
E-mail:lixin97vvv@163.com;fanqing_98@vip.sina.com
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Xin LI, Qing FAN. Application progress of machine learning in the study of facial features of patients with depression[J]. JOURNAL OF SHANGHAI JIAOTONG UNIVERSITY (MEDICAL SCIENCE), 2022, 42(1): 124-129.
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| 1 | AGARWAL S, MUKHERJEE D P. Facial expression recognition through adaptive learning of local motion descriptor[J]. Multimed Tools Appl, 2017, 76(1): 1073-1099. |
| 2 | WHO. The world health report 2001. Mental health: new understanding, new hope[R/OL]. [2021-03-22]. https://www.who.int/whr/2001/en/whr01_en.pdf. |
| 3 | BHATIA S, HAYAT M, BREAKSPEAR M, et al. A video-based facial behaviour analysis approach to melancholia[C]//2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017). Washington, DC, USA: IEEE, 2017: 754-761. |
| 4 | ELLGRING H. Non-verbal communication in depression[M]. Cambridge: Cambridge University Press, 2007. |
| 5 | ROTTENBERG J, GROSS J J, GOTLIB I H. Emotion context insensitivity in major depressive disorder[J]. J Abnorm Psychol, 2005, 114(4): 627-639. |
| 6 | BYLSMA L M, MORRIS B H, ROTTENBERG J. A meta-analysis of emotional reactivity in major depressive disorder[J]. Clin Psychol Rev, 2008, 28(4): 676-691. |
| 7 | PAMPOUCHIDOU A, SIMANTIRAKI O, VAZAKOPOULOU C M, et al. Facial geometry and speech analysis for depression detection[C]//2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Jeju, Korea (South): IEEE, 2017: 1433-1436. |
| 8 | CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA: IEEE, 2017: 1800-1807. |
| 9 | MOLLAHOSSEINI A, CHAN D, MAHOOR M H. Going deeper in facial expression recognition using deep neural networks[C]//2016 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Placid, NY, USA: IEEE, 2016: 1-10. |
| 10 | YADAV Y, KUMAR V, RANGA V, et al. Analysis of facial sentiments: a deep-learning way[C]//2020 International Conference on Electronics and Sustainable Communication Systems (ICESC). Coimbatore, India: IEEE, 2020: 541-545. |
| 11 | JIA C, LI C L, YING Z. Facial expression recognition based on the ensemble learning of CNNs[C]//2020 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC). Macao, China: IEEE, 2020: 1-5. |
| 12 | TANG Y, ZHANG X M, WANG H. Geometric-convolutional feature fusion based on learning propagation for facial expression recognition[J]. IEEE Access, 2018, 6: 42532-42540. |
| 13 | WANG X, WANG Y, ZHOU M, et al. Identifying psychological symptoms based on facial movements[J]. Front Psychiatry, 2020, 11: 607890. |
| 14 | JONITTA MERYL C, DHARSHINI K, SUJITHA JULIET D, et al. Deep learning based facial expression recognition for psychological health analysis[C]//2020 International Conference on Communication and Signal Processing (ICCSP). Chennai, India: IEEE, 2020: 1155-1158. |
| 15 | JIANG Z, HARATI S, CROWELL A, et al. Classifying major depressive disorder and response to deep brain stimulation over time by analyzing facial expressions[J]. IEEE Trans Biomed Eng, 2021, 68(2): 664-672. |
| 16 | MULAY A, DHEKNE A, WANI R, et al. Automatic depression level detection through visual input[C]//2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability WorldS4. London, UK: IEEE, 2020: 19-22. |
| 17 | LI J, LIU Z, DING Z, et al. A novel study for MDD detection through task-elicited facial cues[C]//2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Madrid, Spain: IEEE, 2018: 1003-1008. |
| 18 | GOGATE U, PARATE A, SAH S, et al. Real time emotion recognition and gender classification[C]//2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC). Aurangabad, India: IEEE, 2020: 138-143. |
| 19 | JAN A, MENG H, GAUS Y F B A, et al. Artificial intelligent system for automatic depression level analysis through visual and vocal expressions[J]. IEEE Trans Cogn Dev Syst, 2018, 10(3): 668-680. |
| 20 | CARNEIRO DE MELO W, GRANGER E, LOPEZ M B. Encoding temporal information for automatic depression recognition from facial analysis[C]//ICASSP 2020‒2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Barcelona, Spain: IEEE, 2020: 1080-1084. |
| 21 | CARNEIRO DE MELO W, GRANGER E, HADID A. A deep multiscale spatiotemporal network for assessing depression from facial dynamics[J]. IEEE Trans Affect Comput, 2020. DOI: 10.1109/TAFFC.2020.3021755. |
| 22 | ZHOU X, JIN K, SHANG Y, et al. Visually interpretable representation learning for depression recognition from facial images[J]. IEEE Trans Affect Comput, 2020, 11(3): 542-552. |
| 23 | SWAMY P M, JANARDHAN KURAPOTHULA P, MURTHY S V, et al. Voice assistant and facial analysis based approach to screen test clinical depression[C]//2019 1st International Conference on Advances in Information Technology (ICAIT). Chikmagalur, India: IEEE, 2019: 39-44. |
| 24 | CARNEIRO DE MELO W, GRANGER E, HADID A. Combining global and local convolutional 3D networks for detecting depression from facial expressions[C]//2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019). Lille, France: IEEE, 2019: 1-8. |
| 25 | YANG L. Multi-modal depression detection and estimation[C]//2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). Cambridge, UK: IEEE, 2019: 26-30. |
| 26 | LI X, GUO W, YANG H. Depression severity prediction from facial expression based on the DRR_DepressionNet network[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Seoul, Korea (South): IEEE, 2020: 2757-2764. |
| 27 | KUMAR G A R, KUMAR R K, SANYAL G. Facial emotion analysis using deep convolution neural network[C]//2017 International Conference on Signal Processing and Communication (ICSPC). Coimbatore, India: IEEE, 2017: 369-374. |
| 28 | CARNEIRO DE MELO W, GRANGER E, HADID A. Depression detection based on deep distribution learning[C]//2019 IEEE International Conference on Image Processing (ICIP). Taipei, Taiwan, China: IEEE, 2019: 4544-4548. |
| 29 | JAZAERY MAL, GUO G. Video-based depression level analysis by encoding deep spatiotemporal features[J]. IEEE Trans Affect Comput, 2021, 12(1): 262-268. |
| 30 | WANG Y, MA J, HAO B, et al. Automatic depression detection via facial expressions using multiple instance learning[C]//2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). Iowa City, IA, USA: IEEE, 2020: 1933-1936. |
| 31 | SU M, WU C, HUANG K, et al. Exploring microscopic fluctuation of facial expression for mood disorder classification[C]//2017 International Conference on Orange Technologies (ICOT). Singapore: IEEE, 2017: 65-69. |
| 32 | GUO W, YANG H, LIU Z. Deep neural networks for depression recognition based on facial expressions caused by stimulus tasks[C]//2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). Cambridge, UK: IEEE, 2019: 133-139. |
| 33 | DIBEKLIOĞLU H, HAMMAL Z, COHN J F. Dynamic multimodal measurement of depression severity using deep autoencoding[J]. IEEE J Biomed Health Inform, 2018, 22(2): 525-536. |
| 34 | WEN L, LI X, GUO G, et al. Automated depression diagnosis based on facial dynamic analysis and sparse coding[J]. IEEE Trans Inf Forensics Secur, 2015, 10(7): 1432-1441. |
| 35 | 安昳, 曲珍, 许宁, 等. 面部动态特征描述的抑郁症识别[J]. 中国图象图形学报, 2020, 25(11): 2415-2427. |
| 36 | HARATI S, CROWELL A, MAYBERG H, et al. Discriminating clinical phases of recovery from major depressive disorder using the dynamics of facial expression[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2016, 2016: 2254-2257. |
| 37 | HARATI S, CROWELL A, HUANG Y, et al. Classifying depression severity in recovery from major depressive disorder via dynamic facial features[J]. IEEE J Biomed Health Informatics, 2020, 24(3): 815-824. |
| 38 | TRÉMEAU F, MALASPINA D, DUVAL F, et al. Facial expressiveness in patients with schizophrenia compared to depressed patients and nonpatient comparison subjects[J]. Am J Psychiatry, 2005, 162(1): 92-101. |
| 39 | GIRARD J M, COHN J F, MAHOOR M H, et al. Social risk and depression: evidence from manual and automatic facial expression analysis[J]. Proc Int Conf Autom Face Gesture Recognit, 2013: 1-8. |
| 40 | ALGHOWINEM S, GOECKE R, WAGNER M, et al. Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviors[J]. IEEE Trans Affect Comput, 2018, 9(4): 478-490. |
| 41 | TOTO E, TLACHAC M, STEVENS F L, et al. Audio-based depression screening using sliding window Sub-clip pooling[C]//2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA). Miami, FL, USA: IEEE, 2020: 791-796. |
| 42 | YALAMANCHILI B, KOTA N S, ABBARAJU M S, et al. Real-time acoustic based depression detection using machine learning techniques[C]//2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). Vellore, India: IEEE, 2020: 1-6. |
| 43 | WANG Z, CHEN L, WANG L, et al. Recognition of audio depression based on convolutional neural network and generative antagonism network model[J]. IEEE Access, 2020, 8: 101181-101191. |
| 44 | FANG J, WANG T, LI C, et al. Depression prevalence in postgraduate students and its association with gait abnormality[J]. IEEE Access, 2019, 7: 174425-174437. |
| 45 | GAO S, CALHOUN V D, SUI J. Machine learning in major depression: from classification to treatment outcome prediction[J]. CNS Neurosci Ther, 2018, 24(11): 1037-1052. |
| 46 | FAN Y, YU R, LI J, et al. EEG-based mild depression recognition using multi-kernel convolutional and spatial-temporal Feature[C]//2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Seoul, Korea (South): IEEE, 2020: 1777-1784. |
| 47 | YANG L, JIANG D, SAHLI H. Integrating deep and shallow models for multi-modal depression analysis: hybrid architectures[J]. IEEE Trans Affect Comput, 2021, 12(1): 239-253. |
| 48 | STEPANOV E A, LATHUILIÈRE S, CHOWDHURY S A, et al. Depression severity estimation from multiple modalities[C]//2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom). Ostrava, Czech Republic: IEEE, 2018: 1-6. |
| 49 | HAQUE A, GUO M, MINER A S, et al. Measuring depression symptom severity from spoken language and 3D facial expressions[Z/OL]. [2021-03-22]. https://arxiv.org/pdf/1811.08592.pdf. |
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