Preliminary Findings: Use of CNN Powered Criminal Identification System

Md. Faruk Abdullah Al Sohan, Nusrat Jahan Anannya, A. Nahar, K. Kalpoma
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Abstract

Face is the principal distinguishing attribute of the human body in recognizing an individual. Face recognition is vital for the law enforcement authorities in primary detection and identification of the criminals. However, it becomes very difficult and time-consuming to manually identify every individual in highly populous cities and public places. In this paper, we propose an automatic criminal identification technique face recognition of the criminals using history of their criminal activities. The proposed system will detect the detailed features of human face and recognize the suspect entering a public location. For this purpose, we have built a facial recognition system to apply in Convolutional Neural Networks (CNN). In the simulation, we use the Labeled Faces in the Wild (LFW) dataset to train and test the CNN-powered face recognition system. Preliminary findings prove the feasibility of implementing this system in effective detection and identification of individuals. Such an automatic system will be operative to identify and catch the criminals in public places in a more efficient way.
初步发现:使用CNN供电的犯罪识别系统
人脸是人体识别个体的主要特征。人脸识别对于执法机关对犯罪分子的初步侦查和识别至关重要。然而,在人口密集的城市和公共场所,人工识别每个人变得非常困难和耗时。本文提出了一种利用犯罪嫌疑人的犯罪历史对其进行人脸识别的自动犯罪识别技术。该系统将检测人脸的细节特征,并识别进入公共场所的嫌疑人。为此,我们构建了一个应用于卷积神经网络(CNN)的人脸识别系统。在仿真中,我们使用LFW (Labeled Faces In the Wild)数据集来训练和测试cnn驱动的人脸识别系统。初步研究结果证明,该系统在有效检测和识别个体方面是可行的。这样一个自动系统将会更有效地识别和抓捕公共场所的罪犯。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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