Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Analysis of Facial Expressions: The State of the Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Active Appearance Models
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Describing the emotional states that are expressed in speech
Speech Communication - Special issue on speech and emotion
The role of voice quality in communicating emotion, mood and attitude
Speech Communication - Special issue on speech and emotion
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Facial expression recognition from video sequences: temporal and static modeling
Computer Vision and Image Understanding - Special issue on Face recognition
Analysis of emotion recognition using facial expressions, speech and multimodal information
Proceedings of the 6th international conference on Multimodal interfaces
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Audio-Visual Affect Recognition through Multi-Stream Fused HMM for HCI
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fast Multiple Object Tracking via a Hierarchical Particle Filter
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
IEEE Transactions on Pattern Analysis and Machine Intelligence
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
Descriptive temporal template features for visual motion recognition
Pattern Recognition Letters
AVEC 2011-the first international audio/visual emotion challenge
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Automatic Recognition of Non-Acted Affective Postures
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AVEC 2012: the continuous audio/visual emotion challenge
Proceedings of the 14th ACM international conference on Multimodal interaction
AVEC 2013: the continuous audio/visual emotion and depression recognition challenge
Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge
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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.