Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding


Depression could be a severe psychiatric disorder preventing someone from functioning normally in both work and daily lives. Currently, diagnosis of depression needs in depth participation from clinical consultants. It's drawn abundant attention to develop an automatic system for efficient and reliable diagnosis of depression. Underneath the influence of depression, visual-primarily based behavior disorder is instantly observable. This paper presents a novel method of exploring facial region visual-based nonverbal behavior analysis for automatic depression diagnosis. Dynamic feature descriptors are extracted from facial region subvolumes, and sparse coding is utilized to implicitly organize the extracted feature descriptors for depression diagnosis. Discriminative mapping and decision fusion are applied to additional improve the accuracy of visual-based diagnosis. The integrated approach has been tested on the AVEC2013 depression database and the best visual-based mostly mean absolute error/root mean square error results are achieved.

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