지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.6
- 발행연도
- 2023.12
- 수록면
- 511 - 517 (7page)
- DOI
- 10.5573/IEIESPC.2023.12.6.511
이용수
초록· 키워드
오류제보하기
The facial expression features of experimental subjects were analyzed to explore the differences in facial expression between patients with psychological depression and those with a healthy psychology. Three psychological depression identification and classification models, the Knearest neighbor algorithm, support vector machine, and logistic regression, were constructed to verify the usability of a classification model through facial expression feature analysis and identify college students with psychological depression. The experimental results showed significant differences in the average values of several facial expressions, including AU4, AU7, AU9, AU12, AU18, and AU27. The P values were less than 0.05, so they were used in subsequent model analysis. The results obtained through identification by the classification model showed that the performance of the K-nearest neighbor algorithm in the mean absolute error (MAE), root mean square error (RMSE), and accuracy were the poorest, followed by the logistic regression and support vector machine. The MAE, RMSE, and accuracy of the K-nearest neighbor algorithm were 7.14, 9.37, and 83.61%, respectively; the values of the logistic regression algorithm were 6.83, 8.79, and 91.97%, respectively; the values of the support vector machine were 6.23, 7.76, and 94.80%, respectively. This study showed that a classification model constructed using facial expression features could be used to identify and classify college students with psychological depression.
목차
Abstract
1. Introduction
2. Methods for Depression Recognition and Classification
3. Case Study
4. Discussion
5. Conclusion
References
참고문헌 (16)
참고문헌 신청
H. Yildirim-Celik, S. Eroglu, K. Oguz, G. Karakoc-Tugru, Y. Erdogan, D. Isman-Haznedaroglu, C. Eker, A. S. Gonul, “Emotional context effect on recognition of varying facial emotion expression intensities in depression,” Journal of Affective Disorders, Vol
B. B. Velichkovsky, F. R. Sultanova, D. V. Tatarinov, “Explicit and Implicit Processing of Facial Expressions in Depression,” Experimental Psychology, Vol. 14, No. 2, pp. 24-36, Jan. 2021. Article (CrossRef Link)
E. Porter-Vignola, L. Booij, G. Bossé-Chartier, P. Garel, C. M. Herba, “Emotional Facial Expression Recognition and Depression in Adolescent Girls: Associations with Clinical Features,” Psychiatry Research, Vol. 298, No. 8, pp. 1-8, Jan. 2021. Article (Cr
X. Zhou, Z. Wei, M. Xu, S. Qu, G. Guo, “Facial Depression Recognition by Deep Joint Label Distribution and Metric Learning,” IEEE Transactions on Affective Computing, Vol. 13, No. 3, pp. 1605-1618, Sep. 2020. Article (CrossRef Link)
L. Chen, X. Ma, N. Zhu, H. Xue, H. Zeng, H. Chen, X. Wang, X. Ma, “Facial Expression Recognition With Machine Learning and Assessment of Distress in Patients With Cancer,” Oncology Nursing Forum, Vol. 48, No. 1, pp. 81-93, Jan. 2021. Article (CrossRef Lin
B. B. Velichkovsky, F. R. Sultanova, D. V. Tatarinov, “Explicit and Implicit Processing of Facial Expressions in Depression,” Experimental Psychology, Vol. 14, No. 2, pp. 24-36, Jan. 2021. Article (CrossRef Link)
E. Porter-Vignola, L. Booij, G. Bossé-Chartier, P. Garel, C. M. Herba, “Emotional Facial Expression Recognition and Depression in Adolescent Girls: Associations with Clinical Features,” Psychiatry Research, Vol. 298, No. 8, pp. 1-8, Jan. 2021. Article (Cr
X. Zhou, Z. Wei, M. Xu, S. Qu, G. Guo, “Facial Depression Recognition by Deep Joint Label Distribution and Metric Learning,” IEEE Transactions on Affective Computing, Vol. 13, No. 3, pp. 1605-1618, Sep. 2020. Article (CrossRef Link)
L. Chen, X. Ma, N. Zhu, H. Xue, H. Zeng, H. Chen, X. Wang, X. Ma, “Facial Expression Recognition With Machine Learning and Assessment of Distress in Patients With Cancer,” Oncology Nursing Forum, Vol. 48, No. 1, pp. 81-93, Jan. 2021. Article (CrossRef Lin
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