Smart Attendance System Using Deep Learning Convolutional Neural Network

Pooja, I.; Gaurav, J.; Devi, C. R. Yamuna; Aravindha, H. L.; Sowmya, M.

doi:10.1007/978-3-030-23162-0_31

Cited by 2 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pooja. I et al (2018), proposed an attendance device [8] primarily based totally on face reputation that makes use of Deep Learning technique which become aware of the people primarily based totally on saved Image facts sets and achieved through Image Recognition..…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Algorithm for Face Recognition System Based Smart Attendance System

Sathya¹,

Priya²,

Dhanalakshmi³

2022

ECS Trans.

View full text Add to dashboard Cite

Face recognition aims to detect, track, identify, or verify person from video or images using recognition system. Its application varies from security, authentication, social media, and so on. With the advancement of technologies, still it impedes in accuracy. The challenges involved in face recognition are variations in facial expression involve diverging light, facial changes interfered with facial hair, pose, and so on. This research work presents a new hybrid approach, which improves the accuracy in identification of a person. The accuracy of the proposed work clearly depicts that it can be best suited in real time environments for tracking persons. It can be used for applications like authentication, security, and so on. The proposed work is validated by tracking the student presence. It captures the image of the student and detect the face by pre-processing, extracting the features and recognizing the person. For recognition it uses Local Binary Pattern Histogram (LBPH) algorithm and Haar Cascade classifier. LBP Algorithm uses Histogram, which improves the accuracy of the face recognition. It is integrated with Haar classifier, which uses machine learning algorithm. Adaboost Learning algorithm, which selects small features from large set, which improves the efficiency of recognition.

show abstract

Section: Related Workmentioning

confidence: 99%