detection of crime using dactyloscopy and facial recognition
padma shree s,R. Pavithra,K. ANU M. E,
Published in International Journal of Advanced Research in Computer Science Engineering and Information Technology
ISSN: 2321-3337 Impact Factor:1.521 Volume:6 Issue:3 Year: 30 March,2021 Pages:1447-1452
Abstract
Fingerprint images in crime scene are important clues to solve serial cases. In this paper we present a complete crime scene fingerprint identification system using deep machine learning with Convolutional Neural Network (CNN) and face recognition using Deep Network with Adaptive Threshold. Images are acquired from crime scene using methods ranging from precision photography to complex physical and chemical processing techniques and saved as the database. The images collected from the crime scene are usually incomplete and hence difficult to categorize. Suitable enhancement methods are required for pre-processing the fingerprint images. Minutiae are extracted from the fingerprint images. The features of pre- processed data are fed into the CNN as input to train and test the network. In face recognition feature vector (or embedding) is extracted form an input face image by using a deep network. We assign a threshold to the registered face during each registration, and the thresholds of the other registered faces will be modified accordingly. For recognition, given a query image, we extract its feature embedding and compute the similarity scores between it and all of the other stored embeddings. Then we intended use the similarity scores to determine the identity of the query image. The experimental results demonstrated on database using Open CV-Python shows high accuracy recognition a partial or full fingerprints and face in the criminal database. Final part shows the matched person details like name, mobile number, address, face.
Kewords
CNN – Convolutional Neural Network, ReLU – Rectified Linear Unit, MTCNN - Multi- task Cascaded Convolutional Networks
Reference
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