Facial expression-based analysis on emotion correlations, hotspots, and potential occurrence of urban crimes

Thanks to the development of the Internet of things (IoT) and edge computing, the smart cameras across cities provide a massive amount of image samples with time and location labels, laying a solid basis for deep mining of image information and in-depth decision analysis. Therefore, this paper proposes to convert the images with spatiotemporal labels into quantifiable data on emotions, and apply them to crime prediction. Firstly, human emotions were divided into three categories: negative, neutral, and positive. Then, facial expression recognition (FER) was employed to quantify the portrait data. The emotion features thus acquired were imported to the crime prediction model, enhancing the model’s explanatory power. Finally, our method was compared with kernel density estimation (KDE) on six typical crimes. The results show that introducing emotion data helps to reveal the interaction between emotions and crimes and improve the performance of crime prediction.