FaceTime — Deep learning based face recognition attendance system

Considering the existence of very large amount of available data repositories and reach to the very advanced system of hardware, systems meant for facial identification have evolved enormously over the past few decades. Sketch recognition is one of the most important areas that have evolved as an integral component adopted by the agencies of law administration in current trends of forensic science. Matching of derived sketches to photo images of face is also a difficult assignment as the considered sketches are produced upon the verbal explanation depicted by the eye witness of the crime scene and may have scarcity of sensitive elements that exist in the photograph as one can accurately depict due to the natural human error. Substantial amount of the novel research work carried out in this area up late used recognition system through traditional extraction and classification models. But very recently, few researches work focused on using deep learning techniques to take an advantage of learning models for the feature extraction and classification to rule out potential domain challenges. The first part of this review paper basically focuses on deep learning techniques used in face recognition and matching which as improved the accuracy of face recognition technique with training of huge sets of data. This paper also includes a survey on different techniques used to match composite sketches to human images which includes component-based representation approach, automatic composite sketch recognition technique etc.

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“…In article [32], a new approach for image representation was presented. A face recognition model was developed.…”

Section: A Comprehensive Review On Various Face Recognition System Usmentioning

confidence: 99%

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

Chokkadi¹,

Sudeepa

et al. 2019

IJATCSE

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“…A Deep learning based face recognition attendance system using CNN is proposed by M. Arsenovic et al [11]. The CNN has a few essential steps, which are highly advanced techniques: CNN cascade for detecting a face and generating facial embedding with a CNN.…”

Section: Neural Networkmentioning

confidence: 99%

Gesture Interpretation Control System Using Convolutional Neural Networks

Baldursson

Rasti

Gudmundsson

et al. 2019

2019 International Conference on Biomedical Innovations and Applications (BIA)

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This thesis proposes a non-invasive control system for electrical wheelchairs utilizing facial gestures of individuals captured by a real-time monocular camera. The images are interpreted with a convolutional neural network that achieves up to ~99.6% overall accuracy. The control system uses an embedded system with a graphics processing unit for predicting real-time throughput with fast classification time. This solution offers excellent versatility, where the user can make a gesture to depict a command of his choice.

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“…DCNN relies heavily on the training set. Arsenovic et al [8] developed a DCNN based face recognition as a standalone API to be use with an existing RFID based employee attendance system. The system was employed on a computer platform.…”

Section: Similar Implementationsmentioning

confidence: 99%

Development of A Multi-Client Student Attendance Monitoring System

2019

IJRTE

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A multi-client student attendance student monitoring system was developed. The attendance system consists of the client and the server. The core functions of the client device are verifying student’s identity for attendance recording and monitoring their presence in class. Haar-feature based cascade classifier for object detection and the Scale Invariant Feature Transform Technique (SIFT) technique were implemented for the face authentication process. This paper highlights a full-fledge system architecture with face-based identification implemented on the Raspberry Pi 2 board as the client alongside with RFID authentication for initial identification. The system also has webpage integration for system management. The accuracy achieved was 84% for face verification and 75% for face recognition. The experimental result showed that the recognition rate was affected by inconsistency of wearing glasses, distance between the face and the webcam, lighting condition and the environmental background. A database was setup to store attendance and student information. It is supported with a web application to view, update and analyze the attendance data

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FaceTime — Deep learning based face recognition attendance system

Cited by 117 publications

References 10 publications

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

A Study on various state of the art of the Art Face Recognition System using Deep Learning Techniques

Gesture Interpretation Control System Using Convolutional Neural Networks

Development of A Multi-Client Student Attendance Monitoring System

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