Study of edge detection methods based on palmprint lines

Ali, Mouad M. H.; Yannawar, Pravin; Gaikwad, Ashok T.

doi:10.1109/iceeot.2016.7754902

Cited by 25 publications

(7 citation statements)

References 14 publications

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“…Early palmprint recognition methods relied on handcrafted features extracted from a variety of local cues such as principal lines and landmark points for region of interest extraction [11], [12], [13] and achieved reasonable accuracy on benchmark datasets of the time. However, many of these methods [14], [15], operated on high resolution images (500 ppi or more), obtained from contact-based flatbed scanners collected under controlled settings (e.g., laboratory environment, low degrees of freedom due to flat imaging surface, etc.).…”

Section: A Related Workmentioning

confidence: 99%

C2CL: Contact to Contactless Fingerprint Matching

Grosz

Engelsma

Liu

et al. 2022

IEEE Trans.Inform.Forensic Secur.

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Contactless palmprints are comprised of both global and local discriminative features. Most prior work focuses on extracting global features or local features alone for palmprint matching, whereas this research introduces a novel framework that combines global and local features for enhanced palmprint matching accuracy. Leveraging recent advancements in deep learning, this study integrates a vision transformer (ViT) and a convolutional neural network (CNN) to extract complementary local and global features. Next, a mobile-based, end-to-end palmprint recognition system is developed, referred to as Palm-ID. On top of the ViT and CNN features, Palm-ID incorporates a palmprint enhancement module and efficient dimensionality reduction (for faster matching). Palm-ID balances the trade-off between accuracy and latency, requiring just 18ms to extract a template of size 516 bytes, which can be efficiently searched against a 10,000 palmprint gallery in 0.33ms on an AMD EPYC 7543 32-Core CPU utilizing 128-threads. Cross-database matching protocols and evaluations on large-scale operational datasets demonstrate the robustness of the proposed method, achieving a TAR of 98.06% at FAR=0.01% on a newly collected, time-separated dataset. To show a practical deployment of the end-to-end system, the entire recognition pipeline is embedded within a mobile device for enhanced user privacy and security.

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Section: A Related Workmentioning

confidence: 99%

C2CL: Contact to Contactless Fingerprint Matching

Grosz

Engelsma

Liu

et al. 2022

IEEE Trans.Inform.Forensic Secur.

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show abstract

“…In this section, the holistically nested edge detection model and four traditional edge detection operators (Ali et al 2016) are used to extract principal lines. To evaluate their performance, we calculate the structural similarity between results and ground truth images.…”

Section: Principal Line Extractionmentioning

confidence: 99%

“…Palmprint principal line extraction and classification contain three steps: (a) ROI extraction, (b) palmprint principal line extraction and (c) palmprint phenotype classification. Traditional method usually utilizes manually designed filters in these three steps, which needs large labor effort and lacks robustness (Badrinath and Gupta 2012;Ali et al 2016;Bruno et al 2014). And the quality of palmprint images can have a high impact on the classification accuracy (Zhai and Min 2020;Zhai et al 2005).…”

Section: Introductionmentioning

confidence: 99%

Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning

Song

et al. 2022

Phenomics

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Palmprints are of long practical and cultural interest. Palmprint principal lines, also called primary palmar lines, are one of the most dominant palmprint features and do not change over the lifespan. The existing methods utilize filters and edge detection operators to get the principal lines from the palm region of interest (ROI), but can not distinguish the principal lines from fine wrinkles. This paper proposes a novel deep-learning architecture to extract palmprint principal lines, which could greatly reduce the influence of fine wrinkles, and classify palmprint phenotypes further from 2D palmprint images. This architecture includes three modules, ROI extraction module (REM) using pre-trained hand key point location model, principal line extraction module (PLEM) using deep edge detection model, and phenotype classifier (PC) based on ResNet34 network. Compared with the current ROI extraction method, our extraction is competitive with a success rate of 95.2%. For principal line extraction, the similarity score between our extracted lines and ground truth palmprint lines achieves 0.813. And the proposed architecture achieves a phenotype classification accuracy of 95.7% based on our self-built palmprint dataset CAS_Palm.

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“…The main algorithms for feature extractions can be classified into five categories [36]: line-based [36,38], texture-based [37], appearance-based [39], multiple-features-based [40] and orientation-code-based [41] features. Various holistic techniques which learn the useful representations by applying statistical methods [42] have also been proposed.…”

Section: Features Extraction Proceduresmentioning

confidence: 99%

Experimental Validation of a Reliable Palmprint Recognition System Based on 2D Ultrasound Images

Iula

Micucci

2019

Electronics

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Ultrasound has been trialed in biometric recognition systems for many years, and at present different types of ultrasound fingerprint readers are being produced and integrated in portable devices. An important merit of the ultrasound is its ability to image the internal structure of the hand, which can guarantee improved recognition rates and resistance to spoofing attacks. In addition, ambient noise like changes of illumination, humidity, or temperature, as well as oil or ink stains on the skin do not affect the ultrasound image. In this work, a palmprint recognition system based on ultrasound images is proposed and experimentally validated. The system uses a gel pad to obtain acoustic coupling between the ultrasound probe and the user’s hand. The collected volumetric image is processed to extract 2D palmprints at various under-skin depths. Features are extracted from one of these 2D palmprints using a line-based procedure. Recognition performances of the proposed system were evaluated by performing both verification and identification experiments on a home-made database containing 281 samples collected from 32 different volunteers. An equal error rate of 0.38% and an identification rate of 100% were achieved. These results are very satisfactory, even if obtained with a relatively small database. A discussion on the causes of bad acquisitions is also presented, and a possible solution to further optimize the acquisition system is suggested.

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Study of edge detection methods based on palmprint lines

Cited by 25 publications

References 14 publications

C2CL: Contact to Contactless Fingerprint Matching

C2CL: Contact to Contactless Fingerprint Matching

Palmprint Phenotype Feature Extraction and Classification Based on Deep Learning

Experimental Validation of a Reliable Palmprint Recognition System Based on 2D Ultrasound Images

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