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In these studies, many new solutions have been proposed to solve problems in the field of computer vision and human-computer interaction. The first study shows that there is an urgent need for fast driving to find algorithms that can meet the needs of the driver and complement the current system in terms of accuracy and speed. The second paper presents a 3D SIFT descriptor designed for video data and demonstrates its effectiveness in recognition by optimizing the three-dimensional representation of content. The third paper shows new work to increase the discriminatory power of convolutional neural networks (CNN) by changing the target to face recognition, while the fourth paper shows a self-recognition convolutional neural network designed to overcome inherent problems. from real events. The fifth study demonstrates the effectiveness of detecting a drowsy driver and demonstrates the accuracy of data and video input in real time. The sixth and seventh papers provide an in-depth study of local models for nonrigid face recognition and representation and can detect facial pain, respectively. Together, these different areas of research support the advancement of computer vision by solving problems ranging from driver safety to facial recognition and diagnostics.
"Driver Safety System", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.9, Issue 1, page no.98 - 102, January-2024, Available :http://www.ijrti.org/papers/IJRTI2401018.pdf
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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