A New Method for Face Recognition Using Convolutional Neural Network

Kamencay, Patrik; Benčo, Miroslav; Mizdos, Tomas; Radil, Roman

doi:10.15598/aeee.v15i4.2389

Cited by 91 publications

(52 citation statements)

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“…Detail of all parameters is mentioned in [18]. [19] Authors in [9] analyze effectiveness of CNN compared to classical recognition methods including principal component analysis (PCA), local binary pattern histogram (LBPH) and k-nearest neighbor (KNN). They take experiments on ORL (A&T) dataset and their results show that the LBPH model is better than PCA and KNN, but CNN model get the best accuracy of recognition (98.3% compared to other methods, they all less than 90%).…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning

Long¹

2020

IJMECS

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Section: Related Workmentioning

confidence: 99%

A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning

Long¹

2020

IJMECS

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“…The study by Kamencay et al (2017) offers probably the most comprehensive empirical analysis of 3D face recognition. In an attempt to build practical and robust face recognition systems, he proposed three main types of layers for CNN architectures: the convolution layer, the pooling layer, and the fully connected layer.…”

Section: Related Workmentioning

confidence: 99%

“…Our preprocessing method uses HOG features of 3D face texture, different layers of ResNet are created during the experiment and whether decision making in face recognition process can be improved or not is investigated. We evaluated these approaches in the context of the same 3D face-recognition experiment as in (Kamencay et al, 2017), a more challenging task than the face identification task used in (Ahamed, Alam & Manirul Islam, 2018).…”

Section: Related Workmentioning

confidence: 99%

3D texture-based face recognition system using fine-tuned deep residual networks

Zheng

Rahmat

Khalid

et al. 2019

PeerJ Computer Science

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As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.

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“…It offers better performance with respect to speed and accuracy. Most common method of deep learning method is convolution neural network(CNN) [14], a feed forward neural network constituted with many layers such as convolution layer, pooling, rectified linear unit(Re LU layer) and fully connected layers. Convolution layer has independent filters and filters are convolved with the image, are responsible for extraction of features.…”

Section: Deep Learning Approachmentioning

confidence: 99%

Transition from Holistic to Deep learning Face Recognition Methods

Sabeenian*¹,

Harirajkumar²,

Cruz³

2019

IJRTE

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Face recognition, the fastest growing biometric technology of computer vision, made a breakthrough in the field of security, healthcare, access control and marketing etc. This technology helps in automatically discern and identify the faces for authentication by comparing available digital image of faces. Various algorithms have been developed for enhancing the performance of face recognition system. The face authentication system entails three major steps, face detection, feature extraction and face recognition. This paper provides some of the major milestones of face representation for recognition like holistic learning approach, feature based approach, hybrid approach and deep learning approach. The various techniques under these categories are reviewed. Finally, implemented face recognition using convolution neural network (CNN). In this method, the image is captured through webcam for the dataset preparation. The detection is carried out by CNN cascade, followed by face landmark and face embedding by FaceNet CNN. Recognition of face is performed after training the network. Implemented faces recognition successfully and accurately for smaller dataset.

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A New Method for Face Recognition Using Convolutional Neural Network

Cited by 91 publications

References 0 publications

A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning

A Lightweight Face Recognition Model Using Convolutional Neural Network for Monitoring Students in E-Learning

3D texture-based face recognition system using fine-tuned deep residual networks

Transition from Holistic to Deep learning Face Recognition Methods

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