Authors : Shruti Thote; Vidhi Khobragade; Ronit Gajbhiye; Sahil Deshbhratar; Minakshi Ramteke

Volume/Issue : Volume 9 - 2024, Issue 11 - November

Google Scholar : https://tinyurl.com/44n4ajm5

Scribd : https://tinyurl.com/m7umeaa8

DOI : https://doi.org/10.5281/zenodo.14468194

Abstract : Traditional manually drawn forensic sketches often struggle with speed, precision and accuracy used in modern criminal Recognition. To improve these processes, we introduce a research study on an independent application that simplifies the creation and identification of complex sketches. This application replaces the need for forensic sketch artists by offering an easy-to-use drag-and-drop sketch creation interface. The sketches are matched with the criminal database using deep learning for rapid identification of suspects. Our research uses a dual approach. First, a Deep CNN (Deep Convolutional Neural Network) converts sketches into photorealistic images. Second, the face-recognition Python library compares these sketches to police databases, leveraging deep learning models such as ResNet for accurate facial feature matching in real-time. At the core of our method are convolutional neural networks, known for their ability to analyze complex data and extract features. Our framework achieves an average accuracy rate of 0.98, greatly improving the effectiveness of investigations. This approach outperforms existing methods, marking a significant advancement in face sketch identification.

Keywords : Deep Learning, Convolutional Neural Networks, Forensic Science, Face Sketch, Criminal Justice Technology, Face-Recognition Library.

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