Local invariant representation for multi-instance toucheless palmprint identification

Charfi, Nesrine; Trichili, Hanène; Alimi, Adel M.; Solaiman, Basel

doi:10.1109/smc.2016.7844778

Cited by 16 publications

(16 citation statements)

References 17 publications

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“…3) REgim Sfax Tunisia (REST) hand database 2016: The REgim Sfax Tunisia (REST) hand database 2016 (referred to as REST), collected by the Research Groups in Intelligent Machines of Sfax University, contains palmprint samples captured from individuals with ages ranging from 6 to 70 years. The images were captured using a low-cost digital camera following the procedure described in [73]. They are in 24-bit color and have dimensions of .…”

Section: A Databases Usedmentioning

confidence: 99%

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

Genovese

Piuri

Plataniotis

et al. 2019

IEEE Trans.Inform.Forensic Secur.

186

131

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Touchless palmprint recognition systems enable high-accuracy recognition of individuals through less-constrained and highly usable procedures that do not require the contact of the palm with a surface. To perform this recognition, methods based on local texture descriptors and Convolutional Neural Networks (CNNs) are currently used to extract highly discriminative features while compensating for variations in scale, rotation, and illumination in biometric samples. In particular, the main advantage of CNN-based methods is their ability to adapt to biometric samples captured with heterogeneous devices. However, the current methods rely on either supervised training algorithms, which require class labels (e.g., the identities of the individuals) during the training phase, or filters pretrained on general-purpose databases, which may not be specifically suitable for palmprint data. To achieve a high recognition accuracy with touchless palmprint samples captured using different devices while neither requiring class labels for training nor using pretrained filters, we introduce PalmNet, which is a novel CNN that uses a newly developed method to tune palmprintspecific filters through an unsupervised procedure based on Gabor responses and Principal Component Analysis (PCA), not requiring class labels during training. PalmNet is a new method of applying Gabor filters in a CNN and is designed to extract highly discriminative palmprint-specific descriptors and to adapt to heterogeneous databases. We validated the innovative PalmNet on several palmprint databases captured using different touchless acquisition procedures and heterogeneous devices, and in all cases, a recognition accuracy greater than that of the current methods in the literature was obtained.

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Section: A Databases Usedmentioning

confidence: 99%

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

Genovese

Piuri

Plataniotis

et al. 2019

IEEE Trans.Inform.Forensic Secur.

186

131

View full text Add to dashboard Cite

show abstract

“…More recently, the focus in the field of palmprint recognition shifted from traditional feature engineering approaches to the deep learning based solutions [ 1 , 13 , 14 , 25 ]. Fei et al [ 18 ], for example, evaluated a number of feature extraction methods for contactless palmprint recognition, including four deep learning architectures, i.e., AlexNet [ 26 ], VGG-16 [ 27 ], GoogLeNet [ 28 ] and Res-Net-50 [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

“…Genovese et al [ 6 ] introduced the PalmNet architecture, a novel Convolutional Neural Network (CNN) that is capable of fine tuning specific palmprint filters through an unsupervised procedure based on Gabor responses and Principal Component Analysis (PCA), while not requiring class labels during the training process. The authors validated their approach on several publicly available datasets, i.e., the CASIA palmprint database V1 [ 24 ], the IITD database (Version 1.0) [ 23 ], the REgim Sfax Tunisia (REST) hand database 2016 [ 25 ] and the Tongji Contactless Palmprint Dataset [ 5 ]. In all cases, they obtained recognition accuracies greater than those of the prior methods from the literature [ 6 ].…”

Section: Related Workmentioning

confidence: 99%

Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition

Stoimchev

Ivanovska

2021

Sensors

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In the past few years, there has been a leap from traditional palmprint recognition methodologies, which use handcrafted features, to deep-learning approaches that are able to automatically learn feature representations from the input data. However, the information that is extracted from such deep-learning models typically corresponds to the global image appearance, where only the most discriminative cues from the input image are considered. This characteristic is especially problematic when data is acquired in unconstrained settings, as in the case of contactless palmprint recognition systems, where visual artifacts caused by elastic deformations of the palmar surface are typically present in spatially local parts of the captured images. In this study we address the problem of elastic deformations by introducing a new approach to contactless palmprint recognition based on a novel CNN model, designed as a two-path architecture, where one path processes the input in a holistic manner, while the second path extracts local information from smaller image patches sampled from the input image. As elastic deformations can be assumed to most significantly affect the global appearance, while having a lesser impact on spatially local image areas, the local processing path addresses the issues related to elastic deformations thereby supplementing the information from the global processing path. The model is trained with a learning objective that combines the Additive Angular Margin (ArcFace) Loss and the well-known center loss. By using the proposed model design, the discriminative power of the learned image representation is significantly enhanced compared to standard holistic models, which, as we show in the experimental section, leads to state-of-the-art performance for contactless palmprint recognition. Our approach is tested on two publicly available contactless palmprint datasets—namely, IITD and CASIA—and is demonstrated to perform favorably against state-of-the-art methods from the literature. The source code for the proposed model is made publicly available.

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“…A second category of approaches defines the contour of the extracted hand, and the distance from a point of reference (the geometric center [21], [42] or the wrist [43], etc) to the pixels found on the contour [23], [44]- [50]. Considering this distribution of distances, the peaks generally correspond to the tips of the fingers, while the local minimas correspond to the finger valleys.…”

Section: A Standard Palmprint Roi Extractionmentioning

confidence: 99%

“…Charfi et al [50] used a sparse representation of the SIFT descriptors to perform the matching, as well as rank-level fusion with an SVM. Similarly, a rank-level fusion was performed by Chen et al [115] matching SAX and SIFT descriptors.…”

Section: ) Image Descriptors Used For Palmprint Feature Extractionmentioning

confidence: 99%

Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

2020

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Local invariant representation for multi-instance toucheless palmprint identification

Cited by 16 publications

References 17 publications

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

Learning to Combine Local and Global Image Information for Contactless Palmprint Recognition

Toward Unconstrained Palmprint Recognition on Consumer Devices: A Literature Review

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