Abstract
Intelligent surveillance systems are widely used in several critical areas as airports, ATM, and bank agencies to ensure more security and more safety. The need for an intelligent behavior recognition system is still increasing. Traditional approaches based on access to restricted places or suspected actions as theft, scam, and loitering are insufficient to identify suspect behavior. These actions do not represent a real key of suspects. Novel trends attempt to extract behaviors from involuntary actions as face gestures, face characteristics, and feeling features. This paper is motivated not only by the limits of the traditional approaches but also by the complexity of intelligent algorithms. In this context, the present paper uses face features to recognize the feeling of fear like a suspect behavior. Indeed, this feeling represents the main characteristic of a suspicious person under crime as announced by several psychologist scientists. The fear feeling is usually followed by an increase in heart rate beats. This paper describes the recognition of fear feeling using a camera as a contactless sensor. Frequencies associated with face based-video are used to estimate the heart rate according to the fusion of three techniques: bandpass filter, Eulerian transformer, and Lagrangian transformer. The proposed algorithm benefits from the advantages of each technique, but it is challenged by the Real-Time exigence. For this purpose, a Raspberry PI3 board is used in relation to Raspbian Operating System to ensures Real-Time criteria. The proposed is trained according to CK+ dataset. In this paper, contributions attempt to ensure not only a high recognition rate using a non-complex algorithm but also guarantee a real-time computation. Results reveal that the proposed algorithm has the best heart rate estimation in comparison with traditional methods. Hardware results justify the success of the proposed design in terms of resource requirements.