Enhancing Security by Emotion Analysis Approach Using Deep Learning

Emotion recognition can significantly improve threat assessment capabilities by identifying potential threats by identifying emotional states indicative of hostile intent or distress. By recognizing emotions such as anger or fear, security systems can flag potentially dangerous individuals in vulnerable, crowded environments, enabling proactive action to mitigate risks a possible under This study delves into the use of convolutional neural networks (CNNs) for emotion recognition, Ryerson Audio-Visual. The Database of Emotional Speech and Song (RAVDESS) focuses on emotion-specific emotions such as sadness, happiness, disgust, anger, and neutrality. Comparing basic and advanced CNN models through detailed accuracy analysis and performance simulations during training revealed a remarkable improvement in emotion recognition capability. In this study, models moving the face achieved an accuracy rate of 95% compared to 90% for the original CNN. Thus, in the future, this can be used in real applications to detect the subtle emotional content of security applications.