HUMAN FACE RECOGNITION FOR VIDEO SURVEILLANCE USING DEEP LEARNING

Registration ID: IJNRD_196303

Published ID: IJNRD2305584

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Aaditi Anil Jadhav , Aishwarya Chandrashekhar Danve , Shreyas Rajesh Londhe , Madhireddy Sampath Kumar Reddy

Face Detection; Face Recognition; Face Extraction; DBN; MFB; open CV; harr cascade; CNN.

Surveillance cameras are having a great scope of use in public places e.g. streets, intersections, banks, shopping malls, etc. to increase public safety and it only store records or CCTV footages. Though, the monitoring capability of law enforcement agencies has not kept pace. II. One of the critical tasks in video surveillance is detecting anomalous events like traffic accidents, crimes or illegal activities. III. The solution to this problem involves segmentation, extraction and verification of faces and possibly facial features from an uncontrolled background. In deep learning, a CNN (convolutional neural network or ConvNet) is a class of deep neural networks, which are most commonly applied to analyzing visual imagery. IV. The main advantages of this algorithm are uniqueness and approval as compared to others. One of the chief advantages of video surveillance over still frames is that fact accumulation over multiple frames can provide us a better face recognition performance. The need of speed and accuracy to identify is satisfied.

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Aaditi Anil Jadhav, Aishwarya Chandrashekhar Danve, Shreyas Rajesh Londhe, & Madhireddy Sampath Kumar Reddy (May-2023). HUMAN FACE RECOGNITION FOR VIDEO SURVEILLANCE USING DEEP LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(5), f585-f588. https://ijnrd.org/papers/IJNRD2305584.pdf

Volume 8 Issue 5, May-2023

Pages : f585-f588

Paper Reg. ID: IJNRD_196303

Published Paper Id: IJNRD2305584

Downloads: 000121985

Research Area: Computer Engineering 

Country: Pune, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2305584.pdf

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