Aggressive actions and anger detection from multiple modalities using Kinect
This work addresses the need for real-time security alerts in high-risk environments like prisons and public protests, though it is incremental as it builds on existing multimodal affect recognition methods.
The paper tackled the problem of detecting aggressive actions and anger from multiple modalities using Kinect to enhance surveillance systems, achieving a 15.2% improvement in precision and 11.7% in recall for anger recognition when behavioral rule-based features were incorporated.
Prison facilities, mental correctional institutions, sports bars and places of public protest are prone to sudden violence and conflicts. Surveillance systems play an important role in mitigation of hostile behavior and improvement of security by detecting such provocative and aggressive activities. This research proposed using automatic aggressive behavior and anger detection to improve the effectiveness of the surveillance systems. An emotion and aggression aware component will make the surveillance system highly responsive and capable of alerting the security guards in real time. This research proposed facial expression, head, hand and body movement and speech tracking for detecting anger and aggressive actions. Recognition was achieved using support vector machines and rule based features. The multimodal affect recognition precision rate for anger improved by 15.2% and recall rate improved by 11.7% when behavioral rule based features were used in aggressive action detection.