International Journal of Progressive Research in Engineering Management and Science
(Peer-Reviewed, Open Access, Fully Referred International Journal)

ISSN:2583-1062
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Paper Details

Analysis of Image Classification using SVM and CNN (KEY IJP************497)

  • Aditya Pandey

Abstract

Abstract In today's digital age, face recognition technologies are critical in a variety of industries. Face recognition is one of the most common biometric systems, used for security, authentication, and identification, among other purposes. Although less accurate than iris and fingerprint detection, face recognition is popular due to its contactless and non-invasive nature. Additionally, facial recognition systems can help with attendance tracking in schools, colleges, and companies. This system intends to create a class attendance solution based on face recognition technology, addressing the inefficiencies of old manual attendance systems that are time-consuming and difficult to maintain, with the possibility of proxy attendance. The need for this automated system is clear. The system comprises four phases: database generation, face detection, face recognition, and attendance updating. The database is created from photographs of the pupils in the class. The Haar-Cascade classifier detects faces, while the Local Binary Pattern Histogram method recognizes them. Faces are detected and recognized from live streaming video in the classroom, and attendance records are transmitted to the appropriate faculty at the conclusion of each session. Keywords: face recognition, face detection, attendance system.

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