Automated Depression Diagnosis Based on Facial Dynamic Analysis and Sparse Coding

Depression is a severe psychiatric disorder preventing a person from functioning normally in both work and daily lives. Currently, diagnosis of depression requires extensive participation from clinical experts. It has drawn much attention to develop an automatic system for efficient and reliable diagnosis of depression. Under the influence of depression, visual-based behavior disorder is readily 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 employed to implicitly organize the extracted feature descriptors for depression diagnosis. Discriminative mapping and decision fusion are applied to further improve the accuracy of visual-based diagnosis. The integrated approach has been tested on the AVEC2013 depression database and the best visual-based mean absolute error/root mean square error results have been achieved.