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Prison facilities, mental correctional institutions, sports bars and placesof public protest are prone to sudden violence and conflicts. Surveillancesystems play an important role in mitigation of hostile behavior andimprovement of security by detecting such provocative and aggressiveactivities. This research proposed using automatic aggressive behavior andanger detection to improve the effectiveness of the surveillance systems. Anemotion and aggression aware component will make the surveillance system highlyresponsive and capable of alerting the security guards in real time. Thisresearch proposed facial expression, head, hand and body movement and speechtracking for detecting anger and aggressive actions. Recognition was achievedusing support vector machines and rule based features. The multimodal affectrecognition precision rate for anger improved by 15.2% and recall rate improvedby 11.7% when behavioral rule based features were used in aggressive actiondetection.

Aggressive actions and anger detection from multiple modalities using Kinect