Age and Gender Classification from Finger Vein Patterns

Damak, Wafa; Trabelsi, Randa Boukhris; Masmoudi, Alima Damak; Sellami, Dorra; Naït‐Ali, Amine

doi:10.1007/978-3-319-53480-0_80

Cited by 7 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some studies have been already conducted with the aim of investigating whether it is possible to perform gender recognition based on hand vein patterns. In an early attempt [23], it has been shown that information about the gender of subjects can be extracted from finger vein patterns by resorting to local binary patterns (LBP) features, and using a K -nearest-neighbour (KNN) classifier. Tests performed over the MMCBNU finger vein database [24], comprising images of finger vein patterns taken from 100 volunteers coming from 20 countries, have shown the possibility of reaching a gender recognition accuracy greater than 95%.…”

Section: Hand Vein Patternsmentioning

confidence: 99%

“…Tests performed over the MMCBNU finger vein database [24], comprising images of finger vein patterns taken from 100 volunteers coming from 20 countries, have shown the possibility of reaching a gender recognition accuracy greater than 95%. The work in [23] has been also recently extended, using centre symmetric LBP descriptors and weighted KNNs, to investigate palm vein patterns [25]. Tests have shown the possibility of achieving an accuracy of about 95.8% over the public VERA dataset, which contains images of left and right-hand vein patterns recorded from 110 subjects [26].…”

Section: Hand Vein Patternsmentioning

confidence: 99%

“…However, as shown in [27], such performance is attainable only when training the employed classifiers with images from all the subjects in the VERA dataset, and including samples from both the available recording sessions in the training datasets. Conversely, when only samples from a single session are employed for training purposes, the achievable recognition rates notably worsen, with the approaches in [23] and [25] significantly outperformed by the method proposed in [27], which relies on a shallow neural network based on regularized extreme learning machines (ELMs), able to achieve an accuracy of 93.40% on the VERA dataset.…”

Section: Hand Vein Patternsmentioning

confidence: 99%

See 2 more Smart Citations

Gender-Specific Characteristics for Hand-Vein Biometric Recognition: Analysis and Exploitation

2023

View full text Add to dashboard Cite

In recent years, vein-based biometric recognition has received ever-increasing attention from both academia and industry, due to the advantages it offers over traditional biometric traits such as fingerprint, iris, and face. Nonetheless, some issues related to the use of vein biometrics still need to be investigated and understood. Specifically, in this study, we speculate about the gender-related variations in vein patterns, and their effects on biometric verification performance. An analysis on the feasibility of recognizing male and female subjects depending on their hand-vein patterns, and on the level of similarity characterizing the biometric templates extracted from male and female populations, are here carried out considering three different databases. Specifically, the public VERA dataset, containing samples of palm-vein patterns, and two datasets containing images of finger-vein patterns, i.e., the UTFVP public database, and an in-house dataset collected with an on-the-move contactless modality, are here considered. The obtained experimental results show that the approach here proposed to perform gender recognition allows to reach an accuracy up to 95.83% on the public finger-vein UTFVP dataset, and to outperform the current state-of-the-art on the public palm-vein VERA dataset, with accuracy at 93.55%. It is also shown that vein-based biometric systems can benefit from the exploitation of information regarding the gender of the considered subjects, with achievable recognition rates that can be significantly improved by designing a biometric verification system relying on gender-specific models for extracting the employed discriminative templates.INDEX TERMS Biometric recognition, gender recognition, vein biometrics, deep learning. I. INTRODUCTIONBiometric technologies are nowadays widely adopted in several applications dedicated to human recognition and identity management. A biometric system collects and exploits physical, behavioural, or cognitive traits, characterized by properties such as universality, uniqueness, permanence, measurability, performance, acceptability, and robustness to circumvention, to generate a set of discriminative features employed as user's identifiers. In the recognition phase, the features extracted from the biometric probe are comparedThe associate editor coordinating the review of this manuscript and approving it for publication was Zahid Akhtar .

show abstract

Section: Hand Vein Patternsmentioning

confidence: 99%

Section: Hand Vein Patternsmentioning

confidence: 99%

Section: Hand Vein Patternsmentioning

confidence: 99%

See 1 more Smart Citation

Gender-Specific Characteristics for Hand-Vein Biometric Recognition: Analysis and Exploitation

2023

View full text Add to dashboard Cite

show abstract

“…Having discussed diseases affecting the vascular layout above, it is obvious that information about these diseases can/could/might be extracted from corresponding sample data or templates, respectively. For finger vein sample data, it has been additionally shown [39] that gender as well as 2-4 age classes can be determined with high accuracy (>95%) based on typical preprocessing and the application of LBP. For dorsal hand vein data, [273] reports that feature representation based on vessel structure, PCA, LBP and SIFT do not allow to correctly discriminate male and female subjects.…”

Section: Disease Impact On Recognition and (Template) Privacymentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Advances in Computer Vision and Pattern Recognition

View full text Add to dashboard Cite

The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

show abstract

“…In recent decades, many studies have been established for automatic estimation of gender from biometric data. Various modalities have been explored in the literature for age and gender recognition, such as behavioral traits, speech [3][4][5][6] and gait [7,8] or physiological traits such as face [9][10][11], iris [12,13], fingerprint [14][15][16], skin [17] and hand veins [18][19][20][21][22]. Nowadays, the COVID-19 pandemic imposes the use of a contactless biometric systems to prevent the spread of the contagious disease efficiently.…”

Section: Introductionmentioning

confidence: 99%

Pyramid histograms of oriented gradient for age and gender recognition using finger veins

Damak

Boukhris

Damak

et al. 2023

Fifteenth International Conference on Machine Vision (ICMV 2022)

View full text Add to dashboard Cite

Nowadays, the COVID-19 pandemic imposes the use of a contactless biometric system to prevent the spread of contagious diseases efficiently. Hand veins are contactless and independent of the body's appearance. However, researches on age and gender estimation by hand veins are very limited. They focused only on age group discrimination and not the exact age. Estimating the age and gender by veins features is a challenging task since hand vein images are poor in quality and subject to variation in illumination. In this paper, a finger vein gender and age recognition system based on Pyramidal histograms of oriented gradient (PHOG) is presented. PHOG can better describe both the local shape and the spatial distribution of the veins as the image is divided into sub-regions at different resolutions for which the HOG descriptor is applied. Experimental validation on finger vein databases MMCBNU 6000 and UTFVP demonstrates the effectiveness of extracted features in gender classification and age estimation including ages from 16 to 72 years with an uncertainty of one year. The middle finger of the left hand provides the best results for both age and gender classification (F-measure 100%) for MMCBNU 6000 database, whereas for UTFVP database, F-measure is about 98,62% for age estimation and 99,47% for gender classification. A comparison study with recent approaches is carried on, showing an improvement of F-measure by 5.76% for age estimation and 1.38% for gender classification.

show abstract

Age and Gender Classification from Finger Vein Patterns

Cited by 7 publications

References 13 publications

Gender-Specific Characteristics for Hand-Vein Biometric Recognition: Analysis and Exploitation

Gender-Specific Characteristics for Hand-Vein Biometric Recognition: Analysis and Exploitation

State of the Art in Vascular Biometrics

Pyramid histograms of oriented gradient for age and gender recognition using finger veins

Contact Info

Product

Resources

About