A Data-Theoretic Approach to Identifying Violent Facial Expressions in Social Crime Contexts
This addresses crime prevention by identifying violent behavior early, but it is incremental as it applies an existing CNN method to a new domain with regional data.
The paper tackles the problem of predicting criminal intentions from facial expressions by developing a Convolutional Neural Network system that uses minimal data from a specific geographic region, achieving more efficient detection before crimes occur.
Human Facial Expressions plays an important role in identifying human actions or intention. Facial expressions can represent any specific action of any person and the pattern of violent behavior of any person strongly depends on the geographic region. Here we have designed an automated system by using a Convolutional Neural Network which can detect whether a person has any intention to commit any crime or not. Here we proposed a new method that can identify criminal intentions or violent behavior of any person before executing crimes more efficiently by using very little data on facial expressions before executing a crime or any violent tasks. Instead of using image features which is a time-consuming and faulty method we used an automated feature selector Convolutional Neural Network model which can capture exact facial expressions for training and then can predict that target facial expressions more accurately. Here we used only the facial data of a specific geographic region which can represent the violent and before-crime before-crime facial patterns of the people of the whole region.