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

Abstract

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 .

SoftVein-WELM: A Weighted Extreme Learning Machine Model for Soft Biometrics on Palm Vein Images

“…However, to the best of our knowledge, few works in the literature evaluate gender-and age-recognition tasks on vein pattern images. Particularly, we have only found four works for palm vein images [6,11,15,16], whereas other authors have experimented with dorsal hand images [12,[37][38][39] and the finger veins [11,40].…”

“…Furthermore, [6] indicated that vein-topology-related features, such as the vein pattern gradient, can provide insights into gender information. Additionally, Kuzu et al [11] used gender-specific features for palm vein recognition. Moreover, the visualization of palm veins is influenced by age and body build due to variations in light intensity caused by differences in hemoglobin levels, water content in the blood, and skin properties [12].…”

“…Despite the above, it is noteworthy that only the VERA dataset [14] includes metadata about the age and gender of individuals. To the best of our knowledge, the existing works include few approaches on gender and age classification through palm vein images [6,11,15,16]. Furthermore, the PolyU Multispectral Palmprint dataset [17] comprises palmprint images with gender labeling.…”

“…This work introduces weighted ELM (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems and improve classification performance. The proposed methodology evaluates two W-ELM approaches on VERA [14] and PolyU [17] datasets, comparing their results against existing methods [6,15], a novel convolutional neural network (CNN)-based approach for palm vein recognition [26], and three deeper CNN architectures evaluated in [11] for gender recognition. The proposed SoftVein-WELM model avoids the extraction of texture descriptors for gender and age single/multi-label classification on palm vein images, which guarantees the robustness and simplicity of the model.…”

Fine-Grained Soft Ear Biometrics for Augmenting Human Recognition

“…Examples of soft biometrics that are extracted from a human body include height, weight, age, eye color, etc. Few studies have shown that when combining soft biometrics with traditional (hard) biometrics, these traits can have practical usage and significant importance in improving recognition performance [15][16][17].…”