Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning AAM fitting through simulation
Pattern Recognition
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Depression is a severe mental health disorder causing high societal costs. Current clinical practice depends almost exclusively on self report and clinical opinion, risking a range of subjective biases. It is therefore useful to design a diagnostic aid to assist clinicians. This project aims at developing a novel multimodal framework for depression analysis. In this PhD work, it is hypothesized that a multimodal affective sensing system can better capture what characterises a person's affective state than single modality systems. The project will explore facial dynamics, head movements, upper body gestures, EEG measures and speech characteristics related to affect, in subjects with major depressive disorders. Integrating the individual sensing modalities, a multimodal approach that show improved performance characteristics over single modality approaches will be developed.