The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Depression recognition based on dynamic facial and vocal expression features using partial least square regression
Abstract
Depression is a typical mood disorder, and the persons who are often in this state face the risk in mental and even physical problems. In recent years, there has therefore been increasing attention in machine based depression analysis. In such a low mood, both the facial expression and voice of human beings appear different from the ones in normal states. This paper presents a novel method, which comprehensively models visual and vocal modalities, and automatically predicts the scale of depression. On one hand, Motion History Histogram (MHH) extracts the dynamics from corresponding video and audio data to represent characteristics of subtle changes in facial and vocal expression of depression. On the other hand, for each modality, the Partial Least Square (PLS) regression algorithm is applied to learn the relationship between the dynamic features and depression scales using training data, and then predict the depression scale for an unseen one. Predicted values of visual and vocal clues are further combined at decision level for final decision. The proposed approach is evaluated on the AVEC2013 dataset and experimental results clearly highlight its effectiveness and better performance than baseline results provided by the AVEC2013 challenge organiser.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Citations
Cites
Histograms of Oriented Gradients for Human Detection
Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Ekman, P., & Friesen, W. V. (2019) · PsycTESTS Dataset · doi:10.1037/t27734-000 · Cited by 6,019
Face Description with Local Binary Patterns: Application to Face Recognition
Ahonen, T., Hadid, A., & Pietikäinen, M. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.244 · Cited by 5,592
Computer Vision: A Modern Approach
Forsyth, D. A., & Ponce, J. (2002) · Cited by 3,728
The recognition of human movement using temporal templates
Bobick, A., & Davis, J. W. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.910878 · Cited by 2,837
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Shan, C., Gong, S., & McOwan, P. W. (2009) · Image and Vision Computing · doi:10.1016/j.imavis.2008.08.005 · Cited by 2,300
Survey on speech emotion recognition: Features, classification schemes, and databases
Ayadi, M. M. H. E., Kamel, M. S., & Karray, F. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.09.020 · Cited by 2,139
SIMPLS: An alternative approach to partial least squares regression
de Jong, S. (1993) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/0169-7439(93)85002-x · Cited by 1,905
Scale for the Assessment of Negative Symptoms
Andreasen, N. C. (1982) · PsycTESTS Dataset · doi:10.1037/t12696-000 · Cited by 1,888
Research works printed but not linked: 6
- 10.5555/645312.648916 · DOI
- W. T. Freeman , W. T. Freeman , M. Roth , and M. Roth . Orientation histograms for hand gesture recognition . In IEEE International Workshop on Automatic Face and Gesture Recognition , pages 296 -- 301 , 1994 . W. T. Freeman, W. T. Freeman, M. Roth, and M. Roth. Orientation histograms for hand gesture recognition. In IEEE International Workshop on Automatic Face and Gesture Recognition, pages 296--301, 1994.
- Health & Consumer Protection Directorate General . Improving the mental health of the population: Towards a strategy on mental health for the european union , 2005 . Health & Consumer Protection Directorate General. Improving the mental health of the population: Towards a strategy on mental health for the european union, 2005.
- Health & Consumer Protection Directorate General . Mental health in the EU , 2008 . Health & Consumer Protection Directorate General. Mental health in the EU, 2008.
- S. Salmans . Depression: questions you have - answers you need . People's Medical Society , 1995 . S. Salmans. Depression: questions you have - answers you need. People's Medical Society, 1995.
- 10.5555/520809.796139 · DOI
Cited by
Sorted by citations
Items 101–200 of 223
Hu, M. Q., Chu, Q., Wang, X., He, L., & Ren, F. (2021) · IEEE Signal Processing Letters · doi:10.1109/lsp.2021.3063609 · Cited by 27
“The typical video-based continuous emotion recognition systems mostly consist of two stages: Spatial feature extraction: In [9], Meng et al.”
MethodYu, Y., Ding, S., Li, X., Xu, Z., Wang, Y., Qu, L., Li, Y., & Yang, S. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3163512 · Cited by 27
Self-citationSingh, J., & Hamid, M. A. (2022) · Cognitive Computation · doi:10.1007/s12559-022-10042-2 · Cited by 28
Assessing speaker independence on a speech-based depression level estimation system
Lopez‐Otero, P., Docío-Fernández, L., & Mateo, C. G. (2015) · Pattern Recognition Letters · doi:10.1016/j.patrec.2015.05.017 · Cited by 26
PointTransform Networks for automatic depression level prediction via facial keypoints
Niu, M., Li, M., & Fu, C. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111951 · Cited by 26
Cost-Sensitive Two-Stage Depression Prediction Using Dynamic Visual Clues
Ma, X., Huang, D., Wang, Y., & Wang, Y. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54184-6_21 · Cited by 25
“Different from the previous studies that use MHH [7], GMM [12], Vector of Local Aggregated Descriptors (VLAD) [21], etc.”
MethodInfluentialSelf-citationMulti-feature deep supervised voiceprint adversarial network for depression recognition from speech
Pan, Y., Shang, Y., Wang, W., Shao, Z., Han, Z., Liu, T., Guo, G., & Ding, H. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105704 · Cited by 24
Depression Detection on Malay Dialects Using GPT-3
Hayati, M. F. M., Ali, M. A. M., & Rosli, A. N. (2022) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes54088.2022.10079554 · Cited by 27
“[21] accompanied their vocal dataset with facial expression in their depression recognition system.”
BackgroundQuantitative comparison of motion history image variants for video-based depression assessment
Pampouchidou, A., Pediaditis, M., Maridaki, A., Awais, M., Vazakopoulou, C.-M., Sfakianakis, S. G., Tsiknakis, M., Simos, P. G., Kostas Marias, A., Yang, F., et al. (2017) · EURASIP Journal on Image and Video Processing · doi:10.1186/s13640-017-0212-3 · Cited by 24
Automatic Depression Level Assessment from Speech By Long-Term Global Information Embedding
Li, Y., Niu, M., Zhao, Z., & Tao, J. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747292 · Cited by 23
A facial depression recognition method based on hybrid multi-head cross attention network
Li, Y., Liu, Z., Zhou, L., Yuan, X., Shangguan, Z., Hu, X., & Hu, B. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1188434 · Cited by 22
Pan, Y., Jiang, J., Jiang, K., & Liu, X. (2024) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3664647.3681227 · Cited by 20
Niu, M., Wang, X., Gong, J., Liu, B., Tao, J., & Schuller, B. W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3533480 · Cited by 22
Features-Enhanced Multi-Attribute Estimation with Convolutional Tensor Correlation Fusion Network
Duan, M., Li, K., Liao, X., Li, K., & Tian, Q. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3355542 · Cited by 20
“…58, 66], for example, automatic detection juveniles in Internet, or surveillance at unusual hours or in unusual places; (ii) face retrieval [3, 4, 41, 46, 57, 59], for example, automatic finding of person(s) of interest with provided attributes in a database; and (iii) social media [44, 45] for…”
BackgroundAgent4Vul: multimodal LLM agents for smart contract vulnerability detection
Jie, W., Qiu, W., Yang, H., Guo, M., Huang, X., Lei, T., Zhang, Q., Zheng, H., & Zheng, Z. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4402-2 · Cited by 20
Self-citationDepression severity prediction from facial expression based on the DRR_DepressionNet network
Li, X., Guo, W., & Yang, H. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313597 · Cited by 19
Self-citationHe, L., Yang, W., Zhao, J., Chen, H., & Jiang, D. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3609776 · Cited by 16
Machine Learning-Based Prediction of Depressive Disorders via Various Data Modalities: A Survey
Li, Q., Liu, X., Hu, X., Ahad, M. A. R., Ren, M., Yao, L., & Huang, Y. (2025) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2025.125393 · Cited by 18
Deep Learning for Depression Recognition with Audiovisual Cues: A ReviewPreprint
He, L., Niu, M., Tiwari, P., Marttinen, P., Su, R., Jiang, J., Guo, C., Wang, H., Ding, S., Wang, Z., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.00610 · Cited by 15
“Regarding modality, speech and video samples [14, 94, 113, 17, 96, 97, 99, 100, 101, 104, 121, 122, 123, 149, 150, 16, 151, 152, 153], physiological signals [100, 154, 154, 155, 156, 157, 158, 159, 160], and text [100, 108] have been employed to improve the performance of depression assessment.”
MethodSelf-citationEnhanced Depression Detection from Facial Cues Using Univariate Feature Selection Techniques
Rathi, S., Kaur, B., & Agrawal, R. K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34869-4_3 · Cited by 14
Local Second-Order Gradient Cross Pattern for Automatic Depression Detection
Niu, M., Tao, J., & Liu, B. (2019) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2019.8925158 · Cited by 14
“Based on these considerations, some researchers [7, 8, 9] attempt to extract the detailed information to achieve the goal.”
BackgroundMethodNan, S., Li, Z., Jin, S., Du, W., & Shen, W. (2025) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.4c03293 · Cited by 12
Analysing the Effective Psychological State of Students using Facial Features
Kumar, S., Varshney, D., Dhawan, G., & Jalutharia, H. (2020) · International Conference on Intelligent Computing and Control Systems (ICICCS) · doi:10.1109/iciccs48265.2020.9120909 · Cited by 12
“When considering data that is in the form of video footage or audio recording/tracks, a measure called the Motion History Histogram (MHH) is used to signify slight changes happening in a person’s face or voice characteristics [8].”
MethodDynamic speech emotion recognition with state-space models
Markov, K., Matsui, T., Septier, F., & Peters, G. W. (2015) · European Signal Processing Conference (EUSIPCO) · doi:10.1109/eusipco.2015.7362750 · Cited by 11
“Some of the techniques presented at these workshops include multi-stage approach based on hidden Markov models [2], multi-scale dynamic cues [3], and Partial Least Square Regression [4].”
MethodAutomatic diagnosis of depression based on attention mechanism and feature pyramid model
Xu, N., Huo, H., Xu, J., Ma, L., & Wang, J. (2024) · PLoS ONE · doi:10.1371/journal.pone.0295051 · Cited by 10
DNet: a depression recognition network combining residual network and vision transformer
Jiang, Z., Xu, K. D., Gao, X., Cao, Y., Zhang, Y., Dong, G., Chen, Y. F., Zhu, X., Zhang, Q., Bi, R., et al. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-07322-0 · Cited by 10
A novel study for MDD detection through task-elicited facial cues
Li, J., Liu, Z., Ding, Z., & Wang, G. (2018) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621236 · Cited by 10
“[19] utilize MHH dynamical features to detect depression from both audio and video.”
MethodPredicting Well-being Using Short Ecological Momentary Audio Recordings
Huang, Y.-N., Zhao, S., Rivera, M. L., Hong, J., & Kraut, R. E. (2021) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3411763.3451629 · Cited by 10
“For example, some studies require professional audio equipment [4, 14, 19] and others need both audio and video data [9, 11, 20].”
BackgroundNeumann, M., Roesler, O., Suendermann‐Oeft, D., & Ramanarayanan, V. (2020) · doi:10.18653/v1/2020.nlpmc-1.7 · Cited by 10
“A number of recent papers have investigated automated speech and machine vision features for predicting severity of depression (see for example France et al., 2000; Joshi et al., 2013; Meng et al., 2013; Jain et al., 2014; Kaya et al., 2014; Nasir et al., 2016; Pampouchidou et al., 2016; Yang et al., 2017).”
BackgroundA Depression Recognition Method Based on the Alteration of Video Temporal Angle Features
Ding, Z., Hu, Y., Jing, R., Sheng, W., & Mao, J. (2023) · Applied Sciences · doi:10.3390/app13169230 · Cited by 10
“Facial expressions and facial movements have a significant impact on nonverbal communication [16–19].”
BackgroundRathi, S., Kaur, B., & Agrawal, R. K. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12420-2 · Cited by 9
“In [40], Meng et al. used Motion History Histogram (MHH) to capture the facial dynamics from videos.”
MethodNiu, M., Tao, J., He, Y., Zhang, S., & Li, M. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3565654 · Cited by 8
“From those results, it can be observed that the high-level features extracted using deep learning methods have more advantages in capturing depression cues compared to the hand-crafted features used in [17], [18], [21], [42], [48], [49].”
Audio Signal-based Depression Level Prediction Combining Temporal and Spectral Features
Fu, X., Li, J., Liu, H., Zhang, M., & Ge, X. (2022) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr56361.2022.9956099 · Cited by 8
“In [11], Meng et al. utilized a set of spectral LLD features to further back to the motion history histogram (MHH) and extract information about changes in vocal expression.”
BackgroundMultimodal depression detection using deep learning in the workplace
Manjulatha, B., & Pabboju, S. (2024) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect60202.2024.10468966 · Cited by 8
“Visual and verbal modality were integrated by Meng et al [27] to predict the severity of depression.”
MethodAudio Visual Multimodal Classification of Bipolar Disorder Episodes
Li, Y., Yang, L., Chen, H., Jiang, D., & Hichem Sahli, B. (2019) · International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW) · doi:10.1109/aciiw.2019.8925023 · Cited by 8
“In [18], the authors adopted MHH on gray face images for depression recognition, which records the gray scale value changes for each pixel in the video.”
MethodComputer-Based PTSD Assessment in VR Exposure Therapy
Tavabi, L., Poon, A., Rizzo, A., & Soleymani, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59990-4_32 · Cited by 8
Classifications of clinical depression detection using acoustic measures in Malay speakers
Azam, H., Hashim, N. N. W. N., Sediono, W., Mukhtar, F., Ibrahim, N., Mokhtar, S. S. S., & Aziz, S. A. (2016) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2016.7843521 · Cited by 8
TCEDN: A Lightweight Time-Context Enhanced Depression Detection Network
Yan, K., Miao, S., Jin, X., Mu, Y., Zheng, H., Tian, Y., Wang, P., Yu, Q., & Hu, D. (2024) · Life · doi:10.3390/life14101313 · Cited by 9
Combining Informative Regions and Clips for Detecting Depression from Facial Expressions
Yuan, X., Liu, Z., Chen, Q., Li, G., Ding, Z., Shangguan, Z., & Hu, B. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10157-0 · Cited by 7
“The models in [20, 23, 24] are traditional methods that directly extract spatial and video-level temporal information from the whole video.”
MethodA Visually Interpretable Convolutional-Transformer Model for Assessing Depression from Facial Images
Li, Y., Liu, Z., Li, G., Chen, Q., Ding, Z., Hu, X., & Hu, B. (2023) · IEEE International Conference on Multimedia and Expo (ICME) · doi:10.1109/icme55011.2023.00051 · Cited by 7
“Models in [18]–[20] are based on handcrafted representations.”
BackgroundEmotional and User-Specific Acoustic Cues for Improved Analysis of Naturalistic Interactions
Siegert, I. (2015) · Künstliche Intell. · doi:10.1007/s13218-015-0394-8 · Cited by 6
“[Meng et al. 2013]).”
Survey on Artificial Intelligence- based Depression Detection using Clinical Interview Data
Begum, S. R., & Sait, S. Y. (2022) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec54921.2022.9951984 · Cited by 7
“They face difficulties like insomnia, nervousness, poor appetite, or suicidal ideation [2].”
BackgroundMethodInfluentialCan an Affect-Sensitive System Afford to Be Context Independent?
Marpaung, A. H., & González, A. J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-57837-8_38 · Cited by 7
Depression Severity Assessment for Adolescents at High Risk of Mental Disorders
Muszyński, M., Zelazny, J. M., Girard, J. M., & Morency, L. (2020) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/3382507.3418859 · Cited by 8
“An example approach to depression prediction is to extract LBPs and EOHs of images as visual features combined with LLDs, e.g., pitch, loudness, jitter, shimmer, and Harmonics to Noise Ratio, and Mel-Frequency-Cepstral-Coefficients (MFCCs) of acoustic signals [28].”
BackgroundMethodMFMamba: A Multimodal Fusion State Space Model for Depression Recognition
Liu, J., Shang, Y., Yang, M., Shao, Z., Lu, J., & Liu, T. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10888951 · Cited by 6
“The results are shown in Table I [20]–[22], MFMamba achieved better results, indicating that MFMamba can more effectively model multimodal information.”
InfluentialZhou, W., Shi, Z., Xie, B., Li, F., Yin, J., Zhang, Y., Hu, L., Li, L., Yan, Y., Wei, X., et al. (2025) · Frontiers in Oncology · doi:10.3389/fonc.2025.1622426 · Cited by 6
“Subsequent advances have produced more sophisticated fusion paradigms, including feature-level (26), decision-level (27), hybrid-level (28), and model-level fusion (29), which constitute current state-of-the-art approaches.”
Video-Based Depression Detection Using Support Vector Machine (SVM)
Yadav, N., Singh, A., & Kumar, D. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-10766-5_25 · Cited by 6
A Multi-Frame Rate Network with Attention Mechanism for Depression Severity Estimation
Wang, R., Guo, J. M., Wang, J., He, L., & Yang, Y. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385423 · Cited by 6
Realtime depression estimation using mid-term audio features
Γιαννακόπουλος, Θ., Smailis, C. V., Perantonis, S. J., & Spyropoulos, C. D. (2014) · International Conference on Artificial Intelligence · Cited by 6
Goubault, É., Verdugo, F., Bailly, F., Begon, M., & Maso, F. D. (2023) · IEEE Transactions on Human-Machine Systems · doi:10.1109/thms.2023.3278874 · Cited by 6
“Consequently, a PLSR model aims to search the multidimensional direction in the predictor space that explains the maximum multidimensional variance direction of the response space [37], [39].”
BackgroundCruz, A. C. (2015) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2818346.2830592 · Cited by 5
“Variations of the motion image concept continue to be used for emotion recognition (MHH in depression recognition [22]).”
BackgroundLow-Rank Constrained Latent Domain Adaptation Co-Regression for Robust Depression Recognition
Tao, J., Xu, H., & Fu, J. (2019) · IEEE Access · doi:10.1109/access.2019.2944211 · Cited by 5
“, face detection/tracking and registration/alignment, facial feature extraction, and classifiers learning [2], [7], [8], [28], [33].”
BackgroundSOS-DR: a social warning system for detecting users at high risk of depression
Tai, C.-H., Fang, Y.-E., & Chang, Y. (2017) · Personal and Ubiquitous Computing · doi:10.1007/s00779-017-1092-3 · Cited by 5
“At a similar period, some works tried to improve the depression diagnosis through multimodal information [9, 17].”
BackgroundMethodHuang, Z., Chen, Y., Ren, F., Hu, M., & Liu, J. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108900 · Cited by 4
Attention-driven contextual feature fusion network for facial videos-based depression recognition
Xiao, H., Liu, X., Zhou, T., Qian, L., & Huang, X. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108806 · Cited by 4
Li, Y., Wang, J., Liu, Z., Zhou, L., Zhang, H., Tang, C., Hu, X., & Hu, B. (2023) · Lecture notes in computer science · doi:10.1007/978-981-99-8469-5_20 · Cited by 4
Khan, M. T., Imran, M. A., & Kanwal, M. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20962-4 · Cited by 4
“A motion history histogram was developed to capture motion features via tallying the count of various movements [ 36].”
Automatic Emotional State Detection using Facial Expression Dynamic in Videos
Meng, H., & Huang, D. (2014) · Smart Science · doi:10.1080/23080477.2014.11665627 · Cited by 4
“Furthermore, other emotional states can also be predicted using emotional state prediction such as pain level [15] and depression [16].”
BackgroundSelf-citationValencia-Segura, K. M., Escalante, H. J., & Villaseñor-Pineda, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-07750-0_20 · Cited by 4
Gaus, Y. F. A., Meng, H., Jan, A., Zhang, F., & Turabzadeh, S. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7284859 · Cited by 4
“PLS is used here because it achieved better performance than SVR in emotion recognition [13].”
MethodSelf-citationBargshady, G., & Goecke, R. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-0376-0_16 · Cited by 3
The inner partial least square: An exploration of the “necessary” dimension reduction
Yin, Y., & Liu, L. (2024) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2024.105356 · Cited by 3
Multimodal Local Global Interaction Networks for Automatic Depression Severity Estimation
Niu, M., Shao, Z., He, Y., Tao, J., & Schuller, B. W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3612697 · Cited by 3
Computer audition for healthcare: A survey on speech analysis
Qian, K., Zhao, Z., Tan, Y., Zhang, W., Cho, M., Zhu, C., Tian, F., Hu, B., Yamamoto, Y., & Schuller, B. W. (2025) · AI Open · doi:10.1016/j.aiopen.2025.10.001 · Cited by 3
Bargshady, G., & Goecke, R. (2023) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta60407.2023.00048 · Cited by 3
“Later, different classic methods such as Partial Least-squares Regression [27], Motion History Histogram [28], Space-Time Interest Points (STIP) [10], [22], Pyramid of Histogram of Gradients (PHOG) [7], and Local Gabor Binary Patterns from Three Orthogonal Planes (LGBP-TOP) [3] were applied for…”
MethodSharma, T., Banerjee, K., & Mathur, S. (2020) · Journal of Emerging Technologies and Innovative Research · Cited by 3
Tao, J., & Xu, H. (2019) · IEEE Access · doi:10.1109/access.2019.2961741 · Cited by 3
“To fulfill effective depression recognition from facial images, three consecutive stages are usually committed in conventional ADR systems [4], [5], [7], [8], [26], [35], [32], [28], i.”
Automated Depression Detection Using CNN and Transformer-Based Pre-trained Language Models
Ramalakshmi, N. R., Kumar, M. G., & Raghavi, S. (2024) · IFIP advances in information and communication technology · doi:10.1007/978-3-031-73617-9_4 · Cited by 2
Li, S., Song, S., & Naqvi, S. M. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3604562 · Cited by 5
Faces of the Mind: Unveiling Mental Health States Through Facial Expressions in 11,427 Adolescents
Xu, X., Zhang, X., & Zhang, Y. (2026) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2026.3656335 · Cited by 5
“Early work relied on traditional machine learning models—such as support vector regression [32], decision trees [33], and logistic regression [34]—in combination with hand-crafted features like LBP [35], [36], LLD [33], and HOG [13].”
Depression Detection Using Deep Learning
Khodave, P., Godge, S., Jadhav, S., & Patole, P. M. U. (2022) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-2556 · Cited by 2
Frontier of Smart Healthcare Engineering Management
Ding, S., Wu, D. D., Zhao, L., & Li, X. (2022) · AI for risks · doi:10.1007/978-981-19-2560-3_2 · Cited by 2
Voice Based Stress Analysis and Detection Using Machine Learning
Kanisha, S., Kumar, E. N., Charitha, N. S. L. S., Mehda, B., S, V. A., & Tilak, R. (2024) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs60874.2024.10717243 · Cited by 2
“[2] By utilizing "training data," explicit programming builds a mathematical model that is capable of making predictions or decisions through machine learning.”
TFCTL: Time–Frequency Calibrated Transfer Learning for cross domain depression detection
Li, D., Ding, L., Yang, Z., Wang, Z., & Zhao, K. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.112344 · Cited by 2
Detecting Negative Emotions to Counter Depression Using CNN
Pathak, P., Gangwar, H., & Agarwal, A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-981-33-6546-9_28 · Cited by 2
Cruz, A., Bhanu, B., & Le, B. (2017) · InTech eBooks · doi:10.5772/65684 · Cited by 2
Tambe, S. P. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem33849 · Cited by 2
Deep Learning-based Recommendation System using User Purchase History
Oh, H., Jeong, J.-M., Nam, Y.-J., Seo, Y.-D., & Lee, E.-J. (2022) · The Journal of Korean Institute of Information Technology · doi:10.14801/jkiit.2022.20.6.117 · Cited by 1
Rana, R. K., Hume, M., Reilly, J., Jurdak, R., & Soar, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1502.02796 · Cited by 2
Multimodal Depression Recognition Using Audio and Visual
Xu, X., Zhang, G., Mao, X., & Lu, Q. (2024) · Communications in computer and information science · doi:10.1007/978-981-97-0903-8_22 · Cited by 1
Shen, J., Wu, J., Xing, Y. T., Hu, M., Wang, X., Li, D., & Zha, W. (2024) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.8255 · Cited by 1
Visual Modality Continuous Emotion Recognition Based on Vision Transformer and Transfer Learning
Chen, Z., & Miao, M. (2024) · International Conference on Intelligent Human-Machine Systems and Cybernetics · doi:10.1109/ihmsc62065.2024.00016 · Cited by 1
“Meng et al.[3] generated motion history histograms (MHH) from a sequence of face frames, and then computed edge orientation histograms (EOH) and local binary patterns (LBP) as the emotion representations.”
BackgroundMulti-time scale feature extraction and attention networks for automatic depression level prediction
Al-gawwam, S., Zaitcev, A., Eissa, M. R., Alshwilli, N., & Benaissa, M. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114052 · Cited by 1
A long–short behavioral evolution modeling for video depression recognition
Xie, W., Zhu, Y., Lin, Y., He, X., Zhang, B., Wang, Y., Liu, X., & Cheng, M. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110517 · Cited by 1
Self-citationSpeech and Music Emotion Recognition Using Gaussian Processes
Markov, K., & Matsui, T. (2015) · SpringerBriefs in statistics · doi:10.1007/978-4-431-55339-7_3 · Cited by 1
Application of Partial Least Squares regression for audio-visual speech processing and modeling
Oleinik, A. (2015) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2015-15-5-886-892 · Cited by 1
“Рассматриваются задачи распознавания эмоций [10, 11], чтения по губам и иденти - фикации личности [12], совместной обработки изображений лиц в видимом и инфракрасном спектрах [13].”
A Multiscale Dynamic Temporal Convolution Network For Continuous Dimensional Emotion Recognition
Hu, M. Q., Sun, J., Wang, X., & An, N. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191861 · Cited by 1
“[27] generate motion history histograms from video frame sequences to calculate edge direction histograms and local binary modes as emotion representations.”
MethodDepression Assessment in Youths Using an Enhanced Deep Learning Approach
Pattnaik, S., Nayak, A., Mishra, S., Brahma, B., & Bhoi, A. K. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-38281-9_13 · Cited by 1
Research on Depression Recognition and Diagnosis Based on Facial Expressions
蓝, 杰. (2024) · Advances in Social Sciences · doi:10.12677/ass.2024.134297 · Cited by 1
Multimodal Prediction of Affective Dimensions via Fusing Multiple Regression Techniques
Huang, D., Ding, W., Xu, M., Ming, H., Dong, M., Yu, X., & Li, H. (2017) · Interspeech (USB) · doi:10.21437/interspeech.2017-1088 · Cited by 2
“In this paper, we studied the fusion of SVR, PLS and DBLSTM-RNN as predictors for multimodal prediction of affect dimensions.”
BackgroundMethodInfluentialSelf-citationAudio-visual speech processing and analysis based on subspace projections
Oleinik, A. (2018) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2018-18-2-243-254 · Cited by 1
Automated screening methods for mental and neuro-developmental disorders
Mohammed, S. (2018) · ORCA Online Research @Cardiff (Cardiff University) · Cited by 1
Al-Hameed, S. (2019) · White Rose eTheses Online (University of Leeds, The University of Sheffield, University of York) · Cited by 1
Sentiment analysis using image, text and video
Chen, Q. (2022) · doi:10.32657/10356/161285 · Cited by 1
Speaker- and Corpus-Independent Methods for Affect Classification in Computational Paralinguistics
Kaya, H. (2014) · INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION · doi:10.1145/2663204.2666284
“Important to note that all the results obtained are better than challenge baselines (14.12 RMSE for audio and 13.61 RMSE for video) and best three performances compare favorably to systems reported at AVEC 2013 challenge workshop [2, 17].”
ResultInfluentialMulti-Modality Human Action Recognition
Zhu, Y. (2016) · doi:10.33915/etd.7054
Применение метода частичных наименьших квадратов для обработки и моделирования аудиовизуальной речи
Oleinik, A. (2015) · Научно-технический вестник информационных технологий, механики и оптики
Individualized Smile Detection Using Transfer Learning
Becht, I. (2017) · Utrecht University Repository (Utrecht University)
Automated Screening for Distress: A Perspective for the FuturePreprint
Rana, R. K., Latif, S., Gururajan, R., Gray, A. D., Mackenzie, G., Humphris, G. M., & Dunn, J. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.09944
Richer, V., & Kollias, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05877
Metadata
- Authors
- Meng, H., Huang, D., Wang, H., Yang, H., AI-Shuraifi, M., & Wang, Y.
- Affiliations
- Brunel University of London (GB)
- Beihang University (CN)
- Venue
- Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
- Publisher
- ACM
- Published
- 2013-10-21
- Pages
- 21-30
- OpenAlex
- W1988088279
- Semantic Scholar
- e638499e3649fb69142208c33abf63093c96b00c
- Cited by
- 225
- References
- 47
- Citations by year
- 2026: 7 · 2025: 24 · 2024: 24 · 2023: 21 · 2022: 23 · 2021: 18 · 2020: 18 · 2019: 21 · 2018: 14 · 2017: 16 · 2016: 10 · 2015: 18 · 2014: 11
- FWCI
- 17.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Keywords
- depression detection, facial expressions, vocal expression, multimodal fusion, partial least squares regression, depression severity prediction, decision fusion, affective computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
