2D and 3D face recognition using convolutional neural network

Hu, Huiying; Shah, Syed Afaq Ali; Bennamoun, Mohammed; Molton, Michael

doi:10.1109/tencon.2017.8227850

Cited by 25 publications

(16 citation statements)

References 25 publications

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“…Second, with the ORL dataset, all three models-AlexNet with SVM, ResNet-50 with SVM, and transfer learning on AlexNet-achieved a higher accuracy than the state-of-the-art models [58] with 91.67% and 93.30%, whereas [51] was 97.5%, and [43] was 95%. We obtained 100% with (ResNet-50 + SVM), 99.17% with (AlexNet + SVM), and transfer learning on AlexNet.…”

Section: Comparison With the State-of-the-art Modelsmentioning

confidence: 93%

“…The results of the experiments indicated that the new model achieved enhanced performance. Hu et al [43] investigated the performance of CNN on 2D and 3D FR systems. In their research, two CNN models were constructed-CNN-1 and CNN-2.…”

Section: Related Workmentioning

confidence: 99%

“…The statistical analysis was performed on the basis of the accuracy, mean, and variance. Table 6 shows the performance of our model and the state-of-the-art models, including BQA [39], Hybrid ConvNet-RBM [40], DeepFace [42], CNN-2+ Raw image [43], Deep CNN [49], CNN + SVM [51], DeepFace system [13], DeepID [53], DeepID2 [54], DeepID2+ [55], global expansion ACNN and global + local expansion ACNN [58], and sparse representation face recognition [59]. First, with the FEI faces dataset, the three models achieved a good accuracy-AlexNet with SVM (97.5%), ResNet-50 with SVM (98.5%), and transfer learning on AlexNet (98.7%).…”

Section: Comparison With the State-of-the-art Modelsmentioning

confidence: 99%

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Deep Convolutional Neural Network-Based Approaches for Face Recognition

2019

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Face recognition (FR) is defined as the process through which people are identified using facial images. This technology is applied broadly in biometrics, security information, accessing controlled areas, keeping of the law by different enforcement bodies, smart cards, and surveillance technology. The facial recognition system is built using two steps. The first step is a process through which the facial features are picked up or extracted, and the second step is pattern classification. Deep learning, specifically the convolutional neural network (CNN), has recently made commendable progress in FR technology. This paper investigates the performance of the pre-trained CNN with multi-class support vector machine (SVM) classifier and the performance of transfer learning using the AlexNet model to perform classification. The study considers CNN architecture, which has so far recorded the best outcome in the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) in the past years, more specifically, AlexNet and ResNet-50. In order to determine performance optimization of the CNN algorithm, recognition accuracy was used as a determinant. Improved classification rates were seen in the comprehensive experiments that were completed on the various datasets of ORL, GTAV face, Georgia Tech face, labelled faces in the wild (LFW), frontalized labeled faces in the wild (F_LFW), YouTube face, and FEI faces. The result showed that our model achieved a higher accuracy compared to most of the state-of-the-art models. An accuracy range of 94% to 100% for models with all databases was obtained. Also, this was obtained with an improvement in recognition accuracy up to 39%.

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Section: Comparison With the State-of-the-art Modelsmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: Comparison With the State-of-the-art Modelsmentioning

confidence: 99%

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Deep Convolutional Neural Network-Based Approaches for Face Recognition

2019

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“…Deep learning algorithms have received increasing attention in the face recognition field, and many researchers discovered the importance of studying 3D face recognition (Maiti, Sangwan & Raheja, 2014;Min et al, 2012;Pabiasz, Starczewski & Marvuglia, 2015;Porro-Munoz et al, 2014;Hu et al, 2017;Sun et al, 2015;Wu, Hou & Zhang, 2017;Tang et al, 2013;Zhang, Zhang & Liu, 2019). On one hand, extracting 3D face information is the key step in 3D face recognition: effective face detection and alignment can increase the overall performance of 3D face recognition, which is critical in both security and commercial 3D face recognition systems.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, the convolutional neural network (CNN) models have been used for 3D face recognition. Hu et al (2017) has proposed a method of customizing convolutional neural networks. Her CNN's layer configuration uses the same principle to design based on the LeCun model (LeCun et al, 1989).…”

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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Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognition

Shah

2020

Image and Video Technology

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2D and 3D face recognition using convolutional neural network

Cited by 25 publications

References 25 publications

Deep Convolutional Neural Network-Based Approaches for Face Recognition

Deep Convolutional Neural Network-Based Approaches for Face Recognition

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

Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognition

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