Palmprint principal lines extraction

Bruno, Alessandro; Carminetti, Paolino; Gentile, Vito; Cascia, Marco La; Mancino, Emanuele

doi:10.1109/bioms.2014.6951535

Cited by 15 publications

(8 citation statements)

References 12 publications

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“…The last procedure, indicate here as "Method D", was described in detail in [39]. It was based on an approach proposed in [65] and consists of a higher number of steps than the previous ones with the aim to achieve more accurate results: In this case, after a first contrast adjustment, the image is scaled down and deformed to a square matrix of 128 × 128 pixels to perform in a more effective way filter and transformation along diagonal directions. The image obtained is filtered using Frost filter and then another contrast adjustment is performed.…”

Section: A 2d Feature Extractionmentioning

confidence: 99%

A Feasible 3D Ultrasound Palmprint Recognition System for Secure Access Control Applications

Iula

Micucci

2021

IEEE Access

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Biometric recognition systems based on 3D palmprint captured with optical technology have been widely investigated in the last decade; however, they can provide information about the external skin surface only. This limit can be overcome by Ultrasound, which allows gaining information on the depth of palm lines and can verify the liveness of the sample, making the recognition systems very hard to fake. In this work, a feasible palmprint recognition system based on 3D ultrasound images is proposed. Unlike previous wet setups, the coupling between probe and human is realized through a gel pad, which permits a comfortable and precise positioning of the hand by the user. Collected 3D images are processed to generate 2D palmprint images at various under-skin depths. 2D features are then extracted from these images, experimenting with different procedures, and are merged to define a 3D template that contains lines' depth information. Recognition performances were evaluated by performing verification and identification experiments on a home-made database composed of 423 samples from 55 volunteers. An EER rate of 0.36% and an identification accuracy of 100% are obtained. The suitability of the proposed system in secure access control applications is finally discussed.

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Section: A 2d Feature Extractionmentioning

confidence: 99%

A Feasible 3D Ultrasound Palmprint Recognition System for Secure Access Control Applications

Iula

Micucci

2021

IEEE Access

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“…The former approach analyzes the structure of the palmprint, where generally only principal lines, wrinkles, and texture are well evident, while the latter approach tries to find the ridges or minutiae-like point features to represent the palm pattern. Palm lines include both principal lines and wrinkles, but principal lines get more attention because of their stable and highly distinctive nature [5], [34]. Hence, most authors focus on palm lines when analyzing palmprint images, and we will also focus on low-resolution methods in the following.…”

Section: Related Workmentioning

confidence: 99%

Biometric Palmprint Verification: A Dynamical System Approach

Palma

Montessoro

Giordano

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-Most of the existing techniques for palmprint recognition rely on metrics, typically based on static functions, which evaluate the distance between a pair of features. In this paper, we propose a new technique for palmprint verification based on a dynamical system approach for principal palm lines matching. The proposed dynamic algorithm is recursive and involves a positive linear dynamical system, whose evolution depends on the matching level between the two input images. In a preprocessing phase, the procedure iteratively erodes both of the images to be compared, by eliminating points in each image that do not have enough close neighboring points both in the image itself and the comparison image. As a result of the iterations, only the points that have enough neighboring points in both the image itself and in the comparison image can survive. Thus, the output of the dynamical system converges either to zero, when a deep mismatch exists between the two images, or to a high value, when a good matching is observed. The results, in terms of verification, are in line with the state-of-the-art results in the current literature. The main advantage of the approach is its robustness when dealing with low-resolution and noisy images. The impact of noise (e.g., salt and pepper noise) is effectively reduced: images corrupted with such noise are easily recognized, while a randomly generated image is rejected even when compared with itself.

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“…Three longest and widest lines are known as principle lines and other lines are called wrinkles. Hence, the principle lines, wrinkles and texture are the most dominant features of low resolution palmprint images [13,14]. However, the ridges of the palm cannot be visible with low resolution images, hence; there comes the requirement of high resolution images.…”

Section: Concept Of Palmprint Imagesmentioning

confidence: 99%

A Comparative Analysis of Different Feature Extraction Techniques for Palm-print Images

Pratyasha

Swarnkar

Padhy

2020

Preprint

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In this advanced decade, automatic identification of individuals is a significant achievement due to the high demand of security system. Hence, individual recognition using biometrics data is leading in the field of image processing. Although biometrics data analysis using thumb impression and finger-prints are very popular since many years, sometimes it leads to false acceptance and rejection if any physical change occurs in the finger ridges. There may be a high risk of hacking the biometrics data which is now a big challenge for cyber security employees. This paper captures the palm-print images of individuals as referred biometrics data for individual recognition. The research work is based on one of the prior issue that is feature extraction to extract the features of palm-print image such as principle lines, textures, ridges and pores etc. For this, some of the feature extraction techniques such as Derivatives of Gaussian filter (DoG), Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and competitive coding. Two types of filters: Gaussian Filter and Gabor filter are combined with each of the feature extraction scheme for the matching of sampled image with testing image. In the result, the error rates of each of the feature extraction algorithms are compared to recognize the palm image of two different individuals.

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Palmprint principal lines extraction

Cited by 15 publications

References 12 publications

A Feasible 3D Ultrasound Palmprint Recognition System for Secure Access Control Applications

A Feasible 3D Ultrasound Palmprint Recognition System for Secure Access Control Applications

Biometric Palmprint Verification: A Dynamical System Approach

A Comparative Analysis of Different Feature Extraction Techniques for Palm-print Images

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