A Detection Method of Palmprint Principal Lines Based on Local Minimum Gray Value and Line Following

Yuan, Weiqi; Lin, Sen; Tong, Haibin; Liu, Shudong

doi:10.1109/ichb.2011.6094300

Cited by 2 publications

(1 citation statement)

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“…), and then, based on the diversity and contrast, the detected palmprint lines were refined, and edge tracking method was used to remove the thin branches and short lines to obtain the mainline of the palmprint. Yuan et al [19] considered the a prior knowledge of the specific direction of the principal lines and their valley edges, a dashed line linking algorithm was designed using the tracking method of the principal lines. Kalluri et al [20] designed a set of wide principal line extractors using morphological operators and grouping functions for noise elimination.…”

Section: Related Researchmentioning

confidence: 99%

Improved high-resolution network for palmprint principal line extraction

Jia,

zhou

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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Recently, there has been growing interest in deep learning-based semantic segmentation. We propose a high-resolution network UC-HRNet for palmprint principal line extraction. Compared with other popular deep learning semantic segmentation networks, the performance is stronger. Dense prediction tasks rely heavily on both high-resolution imagery and sophisticated semantic representations for optimal performance. Generally, low-resolution feature maps excel in conveying robust semantic representations, whereas high-resolution feature maps excel in capturing fine-grained details like edges but may lack in semantic richness. Current cutting-edge frameworks, like HRNet, preserves the parallelism between low-and high-resolution feature maps and iteratively exchanges information across different resolutions. The success of UNet is mainly due to its U-shaped structure as well as jump connections.The symmetric U-shaped structure allows for a more thorough fusion of front and back features, it turns out that skip connections are effective in recovering complex details of target objects. We propose a kind of U-shaped structure based on HRNet and add skip connections to make it able to segment the target more accurately. We utilize deep supervision to acquire hierarchical representations from fully aggregated feature maps. Compared to other popular semantic segmentation networks, UC-HRNet proves its effectiveness on three master libraries of semantically segmented palmprints created by ourselves.

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Section: Related Researchmentioning

confidence: 99%

Improved high-resolution network for palmprint principal line extraction

Jia,

zhou

2024

Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024)

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Palmprint principal lines extraction

Bruno

Carminetti

Gentile

et al. 2014

2014 IEEE Workshop on Biometric Measurements and Systems for Security and Medical Applications (BIOMS) Proceedings

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Abstract-The palmprint recognition has become a focus in biological recognition and image processing fields. In this process, the features extraction (with particular attention to palmprint principal line extraction) is especially important. Although a lot of work has been reported, the representation of palmprint is still an open issue. In this paper we propose a simple, efficient, and accurate palmprint principal lines extraction method. Our approach consists of six simple steps: normalization, median filtering, average filters along four prefixed directions, grayscale bottom-hat filtering, combination of bottom-hat filtering, binarization and post processing. The contribution of our work is a new method for palmprint principal lines detection and a new dataset of hand labeled principal lines images (that we use as ground truth in the experiments). Preliminary experimental results showed good performance in terms of accuracy with respect to three methods of the state of the art.

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A Detection Method of Palmprint Principal Lines Based on Local Minimum Gray Value and Line Following

Cited by 2 publications

References 12 publications

Improved high-resolution network for palmprint principal line extraction

Improved high-resolution network for palmprint principal line extraction

Palmprint principal lines extraction

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