A Review on Contactless Hand Verification Systems

Biometric system is gaining popularity increasingly since it provides the most sophisticated technology for authentication, verification, and identification. This technology can identify each individual person on the basis of their biometric information such as their face, hand features, signatures, DNA, or iris pattern and thus can impart a secure and convenient method for authentication purposes. Hand biometrics is one of the most widely used biometric systems. There are two approaches for capturing hand biometrics: contact-based and contactless. In this paper, we present a thorough review of the state of the art contactless hand verification systems. We also present the various modules of the general contactless hand verification system and analyze various hand biometrics features.

Contactless Multi-biometric System Using Fingerprint and Palmprint Selfies

Due to the COVID-19 pandemic, automated contactless person identification based on the human hand has become very vital and an appealing biometric trait. Since, people are expected to cover their faces with masks, and advised avoiding touching surfaces. It is well-known that usually contact-based hand biometrics suffer from issues like deformation due to uneven distribution of pressure or improper placement on sensor, and hygienic concerns. Whereas, to mitigate such problems, contactless imaging is expected to collect the hand biometrics information without any deformation and leading to higher person recognition accuracy; besides maintaining hygienic and pandemic concerns. Towards this aim, in this paper, an effective multi-biometric scheme for person authentication based on contactless fingerprint and palmprint selfies has been proposed. In this study, for simplicity and efficiency, three local descriptors, i.e., local phase quantization (LPQ), local Ternary patterns (LTP), and binarized statistical image features (BSIF), have been employed to extract salient features from contactless fingerprint and palmprint selfies. The score level fusion based multi-biometric system developed in this work combines the matching scores using two different fusion techniques, i.e., transformation based-rules like triangular norms and classifier based-rules like SVM. Experimental results on two publicly available databases (i.e., PolyU contactless to contact-based fingerprint database and IIT-Delhi touchless palmprint dataset) show that the proposed contactless multi-biometric selfie system can easily outperform uni-biometrics.

Mathematical models and meanings by school and university students in a modelling task

This study involves two classes from different educational levels, namely 9th grade and university. Students in both contexts were given a modelling task that required the development of a hand biometrics recognition system, during which they performed experimentation and simulation. As aims of the study, we look for distinctions and commonalities between the models developed in the two classes and seek to know how simulation and experimentation influence students’ production of meaning. The theoretical framework comprises the relationship between the modelling process and the prototyping process and adopts Peirce’s pragmatic perspective on meaning. The research is of a qualitative nature, assuming the characteristics of a case study. The results reveal many commonalities between the modelling in the two contexts. Moreover, experimentation and simulation were relevant elements for the production of meaning by the students, which is endorsed by a pragmatic perspective on meaning.

Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance

Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics, especially in the local feature representation phase. However, the study of acutance in this application has not received a lot of attention. Therefore, in this paper we propose that there is an optimal range of image acutance in hyperspectral hand biometrics. To locate this optimal range, a thresholded pixel-wise acutance value (TPAV) is firstly proposed to assess image acutance. Then, through convolving with Gaussian filters, a hyperspectral hand image was preprocessed to obtain different TPAVs. Afterwards, based on local feature representation, the nearest neighbor method was used for matching. The experiments were conducted on hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) databases containing 53 bands. The results that achieved the best performance were those where image acutance was adjusted to the optimal range. On average, the samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the HDHV and HPV datasets, respectively. Furthermore, our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral. From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics.