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Criminal identification remains a crucial aspect of law enforcement, but existing face recognition techniques often struggle with accuracy due to variations in facial features, lighting conditions, and occlusions. Traditional approaches, such as Haar Cascade and Histogram of Oriented Gradients with K-Nearest Neighbors, exhibit limitations in precision and robustness. This paper proposes an enhanced AI-driven system that integrates HOG and Convolutional Neural Networks (CNNs) for feature extraction, combined with a hybrid KNN-Support Vector Machine (SVM) classification model. Additionally, Multi-task Cascaded Convolutional Neural Networks (MTCNNs) are used for facial alignment. The proposed system significantly out performs conventional approaches in terms of Classification Models, making it highly effective for law enforcement applications
Keywords:
Face Recognition, Criminal Identification, HOG, CNNs, KNN, SVM, Biometric Security.
Cite Article:
"AI Based Criminal Identification System", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.a428-a432, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503055.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator