Evaluating Musculoskeletal Complications Among Individuals With Diabetes Mellitus

Dr. Simran Maheshwari; Dr. Subhan Quraishi; Dr. Babita Ghodke

Volume : II, Issue : XI, November - 2013

Evaluating Musculoskeletal Complications Among Individuals With Diabetes Mellitus

Dr. Simran Maheshwari, Dr. Subhan Quraishi, Dr. Babita Ghodke

Abstract :

The interaction between human beings and computers will be more natural if computers are able to perceive and respond to human non–verbal communication such as emotions. Although several approaches have been proposed to recognize human emotions based on facial expressions or speech, relatively limited work has been done to fuse these two, and other, modalities to improve the accuracy and robustness of the emotion recognition system. In this paper, to propose an particle swarm optimization (PSO) algorithm based facial action coding system (FACS) for automatic human emotion recognition. The method proposed here enables the detection of a much larger range of facial behavior by recognizing facial muscle actions [action units (AU5)] that compound expressions. AUs are agnostic, leaving the inference about conveyed intent to higher order decision making (e.g., emotion recognition). The proposed fully automatic method not only allows the recognition of 22 AUs but also explicitly models their temporal characteristics (i.e., sequences of temporal segments: neutral, onset, apex, and offset). To do so, it uses a facial point detector based on Gabor–feature–based boosted classifiers to automatically localize 20 facial fiducial points.