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Using Deep Learning approaches, automated facial recognition (AFR) seeks to recognize individuals in pictures or videos. Automatic face detection is frequently utilized in many different applications, from simple authentication systems to sophisticated ones. Since it entails significant fluctuations in both acquisition conditions as well as in facial emotions and pose changes, automatic face recognition of faces acquired by digital cameras in unrestricted, real-world environments is still a very difficult task. The cascade classifiers are well-known methods for face detection that aid in more precise criminal identification. In light of these major difficulties, as well as the created solutions and applications based on Deep Learning techniques, this paper introduces the topic of computer-automated face recognition utilizing the Cascade classifier.
Keywords:
Automated Face Recognition (AFR), Cascade Classifier, Fraud Detection, Deep Learning
Cite Article:
"Real Time System for Recognition and Detection of Face in Fraudulent Behaviour", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 3, page no.520 - 529, March-2023, Available :http://www.ijrti.org/papers/IJRTI2303091.pdf
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000205172
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