Contactless Palmprint Identification Using Deeply Learned Residual Features

Liu, Yang; Kumar, Ajay

doi:10.1109/tbiom.2020.2967073

Cited by 54 publications

(42 citation statements)

References 28 publications

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“…Zhao and Zhang [28] presented a joint constrained least-square regression (JCLSR) model with a deep local convolution feature for palmprint recognition. Zhao et al [29] also proposed a joint deep convolutional feature representation (JDCFR) methodology for hyperspectral palmprint recognition. Liu and Kumar [30] proposed a generalizable deep learning-based framework for the contactless palmprint recognition, in which the network is based on a fully convolutional network that generates deeply learned residual features.…”

Section: D and 3d Palmprint Recognition And Palm Vein Recognition Methods Based On Deep Learningmentioning

confidence: 99%

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Jia

Xia

Min

et al. 2021

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. In recent years, in the field of artificial intelligence, deep learning has gradually become the mainstream recognition technology because of its excellent recognition performance. Some researchers have tried to use convolutional neural networks (CNNs) for palmprint recognition and palm vein recognition. However, the architectures of these CNNs have mostly been developed manually by human experts, which is a time-consuming and error-prone process. In order to overcome some shortcomings of manually designed CNN, neural architecture search (NAS) technology has become an important research direction of deep learning. The significance of NAS is to solve the deep learning model’s parameter adjustment problem, which is a cross-study combining optimization and machine learning. NAS technology represents the future development direction of deep learning. However, up to now, NAS technology has not been well studied for palmprint recognition and palm vein recognition. In this paper, in order to investigate the problem of NAS-based 2D and 3D palmprint recognition and palm vein recognition in-depth, we conduct a performance evaluation of twenty representative NAS methods on five 2D palmprint databases, two palm vein databases, and one 3D palmprint database. Experimental results show that some NAS methods can achieve promising recognition results. Remarkably, among different evaluated NAS methods, ProxylessNAS achieves the best recognition performance.

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Section: D and 3d Palmprint Recognition And Palm Vein Recognition Methods Based On Deep Learningmentioning

confidence: 99%

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Jia

Xia

Min

et al. 2021

Int. J. Autom. Comput.

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show abstract

“…The detection/extraction are generally considered to be the same (as in [35]- [37]) whereas the alternative is to have two stages: one in which hand detection is performed, followed by key-point regression for palmprint extraction (as in [30], [32]). However, since palmprint recognition can benefit from a constrained acquisition protocol (where the hand/palm occupy most of the input image), a hand detection stage is not generally required (as in [33], [38]).…”

Section: Roi Template Detection/extractionmentioning

confidence: 99%

“…Recently, Liu and Kumar [37] also considered a Fast R-CNN [77] for palmprint ROI detection. They acquired several videos of palmprints in 11 environments (no other details provided) where the hand pose was varied (from spread to closed fingers, with several hand orientations).…”

Section: Palmprint Roi Extraction Based On Neural Networkmentioning

confidence: 99%

“…Liu et al [37] introduced the soft-shifted triplet loss as a 2D embedding specifically developed for palmprint recognition (instead of a 1D embedding). Furthermore, translations on x and y axes were used to determine the best candidates for triplet pairs (at batch level).…”

Section: ) Training Dnnsmentioning

confidence: 99%

“…Furthermore, at the time of writing of this literature review, there are currently no CNN-based solutions to detect the palmprint in unconstrained environments, besides the Fast R-CNN approach demonstrated by Liu et al [37], which is a Fast-RCNN.…”

Section: B Palmprint Roi Extractionmentioning

confidence: 99%

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Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

2020

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Robust features fusion utilization for supervised palmprint recognition

Ata

Elgamily²,

Mohamed

2022

Concurrency and Computation

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This article proposes robust features fusion methodology for supervised palmprint recognition. The process of features fusion has been formulated according to the hybridization between robust morphological features of principal lines and the following features extractors: principal component analysis (PCA), transformed domain extractors, and invariant moments. The region of interest (ROI) has been extracted based on the identification of the valley points locations. Moreover, both morphological operations and edge detection have been applied in order to formulate the correct ROI. First, both length and slope of each extracted principal line have been estimated morphologically. Second, different features extraction techniques have been appliedfor the resultant ROI including (PCA), transformed domain, and invariant moments.Finally, seven supervised machine learning methods have been manipulated in order to achieve the highest palmprint recognition accuracy. The accuracy of the recognition was evaluated by measuring sensitivity, dice, precision, Jaccard coefficients, correlation, accuracy, and recognition time. Experimental findings show that, among all tested machine learning methods, the feed forward neural network with back propagation based on features vector of the fusion between invariant moments and principal lines morphology has achieved approximately 99.9% of successful palmprints recognition. K E Y W O R D Sdiscrete cosine transform, discrete wavelet transform, invariant moments, principal component analysis, Hough transform, morphological features, transformed domain.

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Contactless Palmprint Identification Using Deeply Learned Residual Features

Cited by 54 publications

References 28 publications

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

2D and 3D Palmprint and Palm Vein Recognition Based on Neural Architecture Search

Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

Robust features fusion utilization for supervised palmprint recognition

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