A Data-Theoretic Approach to Identifying Violent Facial Expressions in Social Crime Contexts

TL;DR

This paper presents a CNN-based system that predicts violent intentions from facial expressions using minimal data, focusing on regional facial patterns to identify potential crimes before they occur.

Contribution

It introduces a novel CNN approach that automates feature selection for violent facial expression detection with limited regional data.

Findings

Effective detection of violent intentions from facial expressions.

Reduced data requirements compared to traditional methods.

High accuracy in predicting pre-crime facial patterns.

Abstract

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.