Facial Recognition for Criminal Identification using Convolutional Neural Network
V. Sathya Preiya1, R. Vijay2*, A. Hemlathadhevi3, C. Bharathi Sri4
1Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India
2Department of Computer Science, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600062, India
3Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, 600123, India
4Department of Computer Science, Velammal Engineering College, Chennai, 600066, India
Emails: sathyapreiya@yahoo.com; drvijayr@veltech.edu.in; hemlathadhevi@gmail.com; bharathisri89@gmail.com
Abstract
The process of identifying and recognising the criminal is the time consuming and difficult task. There are several ways to identify culprits at the crime site, including fingerprinting, DNA matching, and eyewitness testimony. The criminal face identification system will be built on a existing criminal database. The method for identifying a human face using features extrapolated from an image is presented in this study. The technique for identifying a human face using characteristics extrapolated from a picture is presented in this research. It is quite difficult to develop a computer model for recognizing the human face since it is a complicated multidimensional visual representation. The video captured by the camera will be translated into frames as part of the suggested process. To increase detection accuracy, this suggested a Binary Gradient Alignment (BGA) algorithm a description texture classification technique. When a facial feature is detected in an image frame, it undergoes pre-processing to eliminate unnecessary data and reduce unwanted distortions. The real- time processed image is compared to the trained images that have previously been saved in the database. The technology will send an automatic email notice to the police officials if the surveillance camera detects a criminal.
Keywords: Binary Gradient Alignment (BGA); Face recognition; Concurrent Convolutional Neural Network (CCNN); Image Processing; Amalgam Denoising Algorithm.