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We proposed a framework to detect the video contents of depressed and non-depressed subjects. First we characterized the expressed emotions in the video stream using Gabor wavelet features extracted at the facial landmarks which were detected using landmark model matching algorithm. Depressed and non-depressed class models were constructed using Gaussian Mixture models. Using 8 hours of video recordings, an hour of video recording per subject, and both gender and class balanced, we examined the effectiveness of both gender based and gender independent modeling approaches for depressed and non-depressed content classification. We found that the gender based content modeling approach improved the classification accuracy by 6% compared to the gender independent modeling approach, achieving 78.6% average accuracy.

Elastic Bunch Graph Matching is one of the well known methods proposed for face recognition. In this work, we propose several extensions to Elastic Bunch Graph Matching and its recent variant Landmark Model Matching. We used data from the FERET database for experimentations and to compare the proposed methods. We apply Particle Swarm Optimization to improve the face graph matching procedure in Elastic Bunch Graph Matching method and demonstrate its usefulness. Landmark Model Matching depends solely on Gabor wavelets for feature extraction to locate the landmarks (facial feature points). We show that improvements can be made by combining gray-level profiles with Gabor wavelet features for feature extraction. Furthermore, we achieve improved recognition rates by hybridizing Gabor wavelet with eigenface features found by Principal Component Analysis, which would provide information contained in the overall appearance of a face. We use Particle Swarm Optimization to fine tune the hybridization weights. Results of both fully automatic and partially automatic versions of all methods are presented. The best-performing method improves the recognition rate up to 22.6% and speeds up the processing time by 8 times over the Elastic Bunch Graph Matching for the fully automatic case.