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• W3201328968 abstract "Facial recognition is a biometric technology that uses unique facial features to identify a person. Preventing retail crime, smart advertising, unlocking phone, finding missing person, diagnosis of disease, tracking school attendance are some of the many utilizations of face recognition. Recently, novel work has been done to detect facial emotion to understand the state of mind of a learner in an online class which better helps the instructor to design courses. Since there is no contact required like it is required for fingerprints, facial recognition offers a quick, automatic, and seamless verification experience. Most of the face detection techniques use deep learning and neural networks which require huge datasets for training. Using large datasets and matching one by one is a time taking approach. On the other hand, face identification using local feature extraction provides less accuracy than what is expected. Hence, this directs us to handle huge database, focus on developing new real time algorithms with maximum accuracy. The paper proposes a dynamic real time automated face recognition system to identify face in live video using surveillance camera and label a known person by their name and unknown person as “Unknown”. In the experiment, stacked-image montage is used to reduce the time for training the images. Multi-feature extraction using Fast Feature, Speeded-Up-Robust Features (SURF) and Sum of Absolute Difference (SAD) have proved beneficial in getting high accuracy using very small dataset for face recognition." @default.
• W3201328968 created "2021-09-27" @default.
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• W3201328968 date "2021-09-20" @default.
• W3201328968 modified "2023-09-27" @default.
• W3201328968 title "Real-Time Facial Recognition Using SURF-FAST" @default.
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• W3201328968 doi "https://doi.org/10.1007/978-981-16-2937-2_32" @default.
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