Criminal Face Identification Using Deep Learning and Machine Learning

Rahul Samant; Nidhi Jain; Shubham Rakhunde; Abhijeet Dhanve; Tanay Mahajan

International Journal of Image Processing and Pattern Recognition

Criminal Face Identification Using Deep Learning and Machine Learning

Abstract

Criminal face identification is a crucial task for law enforcement agencies to prevent and solve crimes. The traditional methods for identifying criminals using human expertise and eyewitness testimonies have several limitations, such as subjectivity, inaccuracy, and unreliability. Machine learning and deep learning techniques have yielded promising gains in facial recognition and identification applications in recent years. In this study, we propose a criminal face identification system that combines machine learning and deep learning algorithms to accurately identify criminals from surveillance camera footage. In the proposed system, facial features are extracted using a convolutional neural network (CNN) and a Haar cascade is employed. The proposed system is evaluated on a publicly available dataset, and the results show that it outperforms the state-of- the-art methods in terms of accuracy and robustness.

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How to cite

Rahul Samant, Nidhi Jain, Shubham Rakhunde, Abhijeet Dhanve, Tanay Mahajan. Criminal Face Identification Using Deep Learning and Machine Learning. International Journal of Image Processing and Pattern Recognition. 2023;9(1):28–34.

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Criminal Face Identification Using Deep Learning and Machine Learning

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