LAFIN: A Convolutional Neural Network-based Technique for Singular Point Extraction and Classification of Latent Fingerprints
Asif Iqbal Khan · 2018
Latent fingerprints left at crime scenes are useful evidence in the court of law. Law enforcement agencies have been using latent fingerprints for many years as a reliable forensic evidence for crime scene analysis. However, due to poor quality images and complex image background, current state-of-the-art for automatic latent fingerprint processing is not as reliable as rolled or live scan fingerprints. In this paper, we propose a Convolutional Neural Network (CNN) based model (LAFIN) to classify latent fingerprints into five different classes. The classification result along with fingerprint image is then fed to a mini classifier to extract the singular points (if any) present in the latent fingerprint. The CNN model for latent fingerprint classification is trained on different fingerprint images from IIIT-D latent database and NIST Special database 4. The results on publicly available IIIT-D latent fingerprint database demonstrate the efficiency of the proposed approach.