3D palmprint recognition using unsupervised convolutional deep learning network and SVM classifier

Chaa, Mourad; Akhtar, Zahid; Attia, Abdelouahab

doi:10.1049/iet-ipr.2018.5642

Cited by 18 publications

(7 citation statements)

References 24 publications

(31 reference statements)

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“…In [13], M. Chaa, Z. Akhtar, and A. Attia introduced a three-dimensional palmprint and used it for human classification. The authors of [14], S. Veluchamy, and L. R. Karlmarx, used finger knuckles and finger veins as the novel biometric traits.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

2020

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In the proposed study, we examined a multimodal biometric system having the utmost capability against spoof attacks. An enhanced anti-spoof capability is successfully demonstrated by choosing hand-related intrinsic modalities. In the proposed system, pulse response, hand geometry, and finger–vein biometrics are the three modalities of focus. The three modalities are combined using a fuzzy rule-based system that provides an accuracy of 92% on near-infrared (NIR) images. Besides that, we propose a new NIR hand images dataset containing a total of 111,000 images. In this research, hand geometry is treated as an intrinsic biometric modality by employing near-infrared imaging for human hands to locate the interphalangeal joints of human fingers. The L2 norm is calculated using the centroid of four pixel clusters obtained from the finger joint locations. This method produced an accuracy of 86% on the new NIR image dataset. We also propose finger–vein biometric identification using convolutional neural networks (CNNs). The CNN provided 90% accuracy on the new NIR image dataset. Moreover, we propose a robust system known as the pulse response biometric against spoof attacks involving fake or artificial human hands. The pulse response system identifies a live human body by applying a specific frequency pulse on the human hand. About 99% of the frequency response samples obtained from the human and non-human subjects were correctly classified by the pulse response biometric. Finally, we propose to combine all three modalities using the fuzzy inference system on the confidence score level, yielding 92% accuracy on the new near-infrared hand images dataset.

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Section: Related Workmentioning

confidence: 99%

Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

2020

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“…Table 2 summarizes the existing CNN-based 3D palmprint recognition methods. Generally, in these methods, the CNNs were applied to different 2D representa-tions of 3D palmprints for recognition such as MCI, GCI, and ST. Samai et al [89] proposed to use DCTNet for 3D palmprint recognition. Chaa et al [90] firstly used a single scale retinex (SSR) algorithm to enhance the depth image of 3D palmprint, then used PCANet for recognition.…”

Section: Inception_resnet_v2mentioning

confidence: 99%

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Jia

Gao

Xia

et al. 2020

Int. J. Autom. Comput.

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Palmprint recognition and palm vein recognition are two emerging biometrics technologies. In the past two decades, many traditional methods have been proposed for palmprint recognition and palm vein recognition, and have achieved impressive results. However, the research on deep learning-based palmprint recognition and palm vein recognition is still very preliminary. In this paper, in order to investigate the problem of deep learning based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct performance evaluation of seventeen representative and classic convolutional neural networks (CNNs) on one 3D palmprint database, five 2D palmprint databases and two palm vein databases. A lot of experiments have been carried out in the conditions of different network structures, different learning rates, and different numbers of network layers. We have also conducted experiments on both separate data mode and mixed data mode. Experimental results show that these classic CNNs can achieve promising recognition results, and the recognition performance of recently proposed CNNs is better. Particularly, among classic CNNs, one of the recently proposed classic CNNs, i.e., EfficientNet achieves the best recognition accuracy. However, the recognition performance of classic CNNs is still slightly worse than that of some traditional recognition methods.

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“…The convolutional neural network is a directed acyclic network structure, which is formed by layering and interconnecting many perceptron; including input layer, hidden layer and output layer [33], [34]. The input layer directly accepts the data of the sample, passes through one or more hidden layers, and then propagates forward to the output layer.…”

Section: E Intelligent Recognition Of Medical Motion Image Based On Convolutional Neural Networkmentioning

confidence: 99%

Intelligent Recognition of Medical Motion Image Combining Convolutional Neural Network With Internet of Things

Zhou

Gao

2019

IEEE Access

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For the small-scale motion in medical motion images, the traditional medical motion image intelligent recognition algorithm has low recognition accuracy, and requires a large amount of calculation statistics. There is no self-learning function, which seriously affects the accuracy and speed of medical motion image recognition. Therefore, in order to improve the accuracy of human body small-scale motion recognition in medical motion images and the computational efficiency of large-scale data sets, an intelligent recognition algorithm based on convolutional neural network for medical motion images is proposed. The algorithm first learns the dense trajectory features and depth features, and then further fuses the dense trajectory features with the deep learning features. Finally, the extreme learning machine is applied to the convolutional neural network, and the fused features are further trained as input information of the convolutional neural network, and the features from the bottom layer to the upper layer can be extracted step by step from the raw data of the pixel level. Simulation experiments show that the algorithm can effectively improve the recognition accuracy of small-scale motion in medical moving images and improve the speed of motion.INDEX TERMS Internet of things, convolutional neural network, medical motion image, intelligent recognition.

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3D palmprint recognition using unsupervised convolutional deep learning network and SVM classifier

Cited by 18 publications

References 24 publications

Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

A Performance Evaluation of Classic Convolutional Neural Networks for 2D and 3D Palmprint and Palm Vein Recognition

Intelligent Recognition of Medical Motion Image Combining Convolutional Neural Network With Internet of Things

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