CRIMINAL FACIAL DETECTION USING DEEP LEARNING

Abstract

Deep learning models have enhanced picture-based semantic pattern identification. Facial photos can assess a person's emotional state and character traits. Due to this motive, we are applying a variety of deep learning architectures to try to infer criminal tendency from face pictures. This study used a simple convolutional neural network (CNN) architecture and multiple pre-trained CNN designs, including VGG-16, VGG-19, and InceptionV3. We compared these systems' effectiveness in identifying criminal traits from human faces. Deep learning models were tested using the National Institute of Standards and Technology's public database. (NIST). Our initiative only used men's images to avoid misunderstanding. VGG CNN models performed best, even with low data, detecting criminal faces with 99.5% accuracy. This index includes Image Classification, Facial Images, Personality Traits, Semantic Pattern Recognition, Deep Learning, and Image Processing.