Anti-cheating presence system based on 3WPCA-dual vision face recognition

Winarno, Edy; Hadikurniawati, Wiwien; Amin, Imam Husni Al; Sukur, Muji

doi:10.1109/eecsi.2017.8239115

Cited by 14 publications

(9 citation statements)

References 11 publications

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“…At the time of face recognition, from the existing research, the 3WPCA-MD method was able to produce faster calculation results when compared to the usual PCA method. In addition, the 3WPCA process produces a good face-based recognition system and can be used to find facsimile data with an accuracy of up to 98% [16].…”

Section: Resultsmentioning

confidence: 99%

“…This is because of the support of Mahalanobis Distances Classification, Mahalanobis distance is a prominent approach that is used to improve the results of classification result by exploiting the data structure [15]. In another study conducted by Winarno [16]explained that 3WPCA can be used to anticipate face data forgery with recognition accuracy up to 98% and produce a good attendance system based on face recognition…”

Section: E Face Recognition For E-learningmentioning

confidence: 99%

“…In addition in this study also added a command to display pop-ups as a warning of the process of not finding a defective human face. This is the basis of e-learning in real-time CNN [8] Can evaluate input images at low resolutions to immediately reject non-face areas and carefully process challenging areas at higher resolutions for accurate detection 3WPCA-MD [14] Shows that the resulting accuracy reaches 96% and 3WPCA-MD method is able to increase speed in the Face recognition process with an average of 5-7 milliseconds (ms) 3WPCA [16] In this study explained that 3WPCA is able to produce facial recognition accuracy up to 98% by implementing it into the attendance system based on face recognition. So that it can be used to anticipate fake face.…”

Section: F Critical Analysismentioning

confidence: 99%

See 2 more Smart Citations

Face Detection and Recognition Based E-Learning for Students Authentication: Study Literature Review

Rabiha

Kurniawan

Moniaga

et al. 2018

2018 International Conference on Information Management and Technology (ICIMTech)

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The Authentication process is highly dependent on Face Detection and Face Recognition during learning activities. In addition, in the E-Learning learning system we must also provide accurate and accurate information. The research method used is literature study and analysis. Literature study was carried out on the face of face detection and recognition in real-Time conditions carried out by various previous studies. Furthermore, analysis was carried out in a literature study studio to find suitable methods for the E-Learning environment. From the results of existing literature studies we recommend face detection using Face Countour and the Adaboost Algorithm added to CNN (Convolutional Neural Network)to normalize the facial contour function and normalize lightning in the surrounding environment where a face detection is located. The process is carried out, we use the 3WPCA-MD(Three-Level Wavelet

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Section: Resultsmentioning

confidence: 99%

Section: E Face Recognition For E-learningmentioning

confidence: 99%

Section: F Critical Analysismentioning

confidence: 99%

See 1 more Smart Citation

Face Detection and Recognition Based E-Learning for Students Authentication: Study Literature Review

Rabiha

Kurniawan

Moniaga

et al. 2018

2018 International Conference on Information Management and Technology (ICIMTech)

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“…Edy Winarno et al, [5] presented an face recognition system using dual camera approach in image acquisition by taking left portion and right portion of the face and it is merged to form a complete face. Extracted features by applying 3wpca algorithm, Mahalanobis distance method is used for classification performance is analyzed with Euclidian distance.…”

Section: Sujay S N H S Manjunatha Reddymentioning

confidence: 99%

Extended Local Binary Pattern Features based Face Recognition using Multilevel SVM Classifier

Sujay¹,

Reddy²

2019

IJRTE

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The Face recognition method is one of the authoritative biometric system in recognition methods to recognize the individual, because face is a distinctive biometric trait of an human being and it is the superior method of recognition. This paper proposes a novel Face recognition method by using extended LBP features. The pre-processing is carried out to extract the face area using viola-johns algorithm and all images are resized to 100x100. The LBP operator is applied on resized face images by rotating the each image by 15 degrees, i.e., at 7 degree left and 7 degree right and at zero degree to extract the feature vectors and final features are obtained by applying histogram technique. The SVM classifier is used for matching the database images with test images to measure the performance such as TSR, FAR, FRR & EER. The performance parameters are compared with existing algorithms for YALE and FERET database.

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“…One of the supporting media that can be relied upon in the attendance system using face detection and face recognition is a real time video camera. Camera in the face detection system have the advantage of application flexibility, so they do not require users to make direct contact with the attendance system [6].…”

Section: Introductionmentioning

confidence: 99%

Face recognition for presence system by using residual networks-50 architecture

Pratama

Ginting

Nainggolan

et al. 2021

IJECE

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Presence system is a system for recording the individual attendance in the company, school or institution. There are several types presence system, including the manually presence system using signatures, presence system using fingerprints and presence system using face recognition technology. Presence system using face recognition technology is one of presence system that implements biometric system in the process of recording attendance. In this research we used one of the convolutional neural network (CNN) architectures that won the imagenet large scale visual recognition competition (ILSVRC) in 2015, namely the Residual Networks-50 architecture (ResNet-50) for face recognition. Our contribution in this research is to determine effectiveness ResNet architecture with different configuration of hyperparameters. This hyperparameters includes the number of hidden layers, the number of units in the hidden layer, batch size, and learning rate. Because hyperparameter are selected based on how the experiments performed and the value of each hyperparameter affects the final result accuracy, so we try 22 configurations (experiments) to get the best accuracy. We conducted experiments to get the best model with an accuracy of 99%.

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Anti-cheating presence system based on 3WPCA-dual vision face recognition

Cited by 14 publications

References 11 publications

Face Detection and Recognition Based E-Learning for Students Authentication: Study Literature Review

Face Detection and Recognition Based E-Learning for Students Authentication: Study Literature Review

Extended Local Binary Pattern Features based Face Recognition using Multilevel SVM Classifier

Face recognition for presence system by using residual networks-50 architecture

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