scholarly journals Enhanced Multimodal Biometric Recognition Based upon Intrinsic Hand Biometrics

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1916
Author(s):  
Syed Aqeel Haider ◽  
Yawar Rehman ◽  
S. M. Usman Ali

In the proposed study, we examined a multimodal biometric system having the utmost capability against spoof attacks. An enhanced anti-spoof capability is successfully demonstrated by choosing hand-related intrinsic modalities. In the proposed system, pulse response, hand geometry, and finger–vein biometrics are the three modalities of focus. The three modalities are combined using a fuzzy rule-based system that provides an accuracy of 92% on near-infrared (NIR) images. Besides that, we propose a new NIR hand images dataset containing a total of 111,000 images. In this research, hand geometry is treated as an intrinsic biometric modality by employing near-infrared imaging for human hands to locate the interphalangeal joints of human fingers. The L2 norm is calculated using the centroid of four pixel clusters obtained from the finger joint locations. This method produced an accuracy of 86% on the new NIR image dataset. We also propose finger–vein biometric identification using convolutional neural networks (CNNs). The CNN provided 90% accuracy on the new NIR image dataset. Moreover, we propose a robust system known as the pulse response biometric against spoof attacks involving fake or artificial human hands. The pulse response system identifies a live human body by applying a specific frequency pulse on the human hand. About 99% of the frequency response samples obtained from the human and non-human subjects were correctly classified by the pulse response biometric. Finally, we propose to combine all three modalities using the fuzzy inference system on the confidence score level, yielding 92% accuracy on the new near-infrared hand images dataset.

2014 ◽  
Vol 1030-1032 ◽  
pp. 2382-2385 ◽  
Author(s):  
Lin Lin Fan ◽  
Hui Ma ◽  
Ke Jun Wang ◽  
Yong Liang Shen ◽  
Ying Shi ◽  
...  

Finger vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. Finger vein recognition faces a number of challenges. One critical issue is the performance of finger vein recognition system. To overcome this problem, a finger vein recognition algorithm based on one kind of subspace projection technology is presented. Firstly, we use Kapur entropy threshold method to achieve the purpose of intercepting region of finger under contactless mode. Then the finger vein features were extracted by 2DPCA method. Finally, we used of nearest neighbor distance classifier for matching. The results indicate that the algorithm has good recognition performance.


2013 ◽  
Vol 333-335 ◽  
pp. 555-558
Author(s):  
Jian Wang ◽  
Shuo Guo Li

In order to understand the transmission mechanism at human tissue of near-infrared square-wave pulse (NIRSP), an experimental study of the duty ratio threshold for regular transmission at the fingertip and palm of human hand by NIRSP was carried out by using a couple of simple transmitting and receiving device with tunable duty ratios, and also the light emitting diodes (LED) both at the wavelengths of 850 nm and 940 nm. The thresholds were determined by measuring the waveforms of the changed voltages of NIRSP receiving system as a measurement of the transmission at human tissue. When the receiving waveforms were taken on the certain behavior as the emitting waveforms, the duty ratio threshold for regular transmission at human tissue of NIRSP was observed. The experimental results show that the duty ratio is an important index for the transmission at human tissue of NIRSP. And the duty ratio thresholds for regular transmission both at the fingertip and palm of NIRSP at the wavelength of 850 nm and 940 nm have been given.


Author(s):  
Nahian Rahman ◽  
Carlo Canali ◽  
Darwin G. Caldwell ◽  
Ferdinando Cannella

Dexterous gripper requirements, such as in-hand manipulation is a capability on which human hands are unique at; numerous number of sensors, degree of freedom, adaptability to deal with plurality of object of our hand motivate the researchers to replicate these abilities in robotic grippers. Developments of gripper or grasping devices have been addressed from many perspectives: the use of materials in the gripper synthesis, such as rigid or flexible, the approach of control, use of under-actuated mechanism and so on. Mathematical formulation of grasp modeling, manipulation are also addressed; however, due to the presence non-holonomic motion, it is difficult to replicate the behaviors (achieved in model) in a physical gripper. Also, achieving skills similar to human hand urge to use soft or non rigid material in the gripper design, which is contrary to speed and precision requirements in an industrial gripper. In this dilemma, this paper addresses the problem by developing modular finger approach. The modular finger is built by two well known mechanisms, and exploiting such modular finger in different numbers in a gripper arrangement can solve many rising issues of manipulation.


2020 ◽  
Vol 9 (04) ◽  
pp. 24994-25007
Author(s):  
Oyinloye Oghenerukevwe Elohor ◽  
Akinbohun Folake ◽  
Thompson Aderonke ◽  
Korede Bashir

This work explores the field of biometric finger vein recognition – which is the identification of individuals using the unique vein patterns under their finger skins. This work also includes the implementation of an Android fingerprint biometric system using the Android Near InfraRed (NIR) module, which exists to show the similarities and differences between the two (fingervein and fingerprint) prevalent biometric features. This work thus confirms that finger vein recognition shows great promise as an accurate solution to modern society’s problem of automated personal authentication


2016 ◽  
Vol 53 (4) ◽  
pp. 041005 ◽  
Author(s):  
徐天扬 Xu Tianyang ◽  
惠晓威 Hui Xiaowei ◽  
林森 Lin Sen

2020 ◽  
Vol 39 (6) ◽  
pp. 668-687
Author(s):  
Alessandro Albini ◽  
Giorgio Cannata

This article deals with the problem of the recognition of human hand touch by a robot equipped with large area tactile sensors covering its body. This problem is relevant in the domain of physical human–robot interaction for discriminating between human and non-human contacts and to trigger and to drive cooperative tasks or robot motions, or to ensure a safe interaction. The underlying assumption used in this article is that voluntary physical interaction tasks involve hand touch over the robot body, and therefore the capability to recognize hand contacts is a key element to discriminate a purposive human touch from other types of interaction. The proposed approach is based on a geometric transformation of the tactile data, formed by pressure measurements associated to a non-uniform cloud of 3D points ( taxels) spread over a non-linear manifold corresponding to the robot body, into tactile images representing the contact pressure distribution in two dimensions. Tactile images can be processed using deep learning algorithms to recognize human hands and to compute the pressure distribution applied by the various hand segments: palm and single fingers. Experimental results, performed on a real robot covered with robot skin, show the effectiveness of the proposed methodology. Moreover, to evaluate its robustness, various types of failures have been simulated. A further analysis concerning the transferability of the system has been performed, considering contacts occurring on a different sensorized robot part.


2015 ◽  
Vol 7 (3) ◽  
Author(s):  
Pei-Hsin Kuo ◽  
Jerod Hayes ◽  
Ashish D. Deshpande

Passive properties of the human hands, defined by the joint stiffness and damping, play an important role in hand biomechanics and neuromuscular control. Introduction of mechanical element that generates humanlike passive properties in a robotic form may lead to improved grasping and manipulation abilities of the next generation of robotic hands. This paper presents a novel mechanism, which is designed to conduct experiments with the human subjects in order to develop mathematical models of the passive properties at the metacarpophalangeal (MCP) joint. We designed a motor-driven system that integrates with a noninvasive and infrared motion capture system, and can control and record the MCP joint angle, angular velocity, and passive forces of the MCP joint in the index finger. A total of 19 subjects participated in the experiments. The modular and adjustable design was suitable for variant sizes of the human hands. Sample results of the viscoelastic moment, hysteresis loop, and complex module are presented in the paper. We also carried out an error analysis and a statistical test to validate the reliability and repeatability of the mechanism. The results show that the mechanism can precisely collect kinematic and kinetic data during static and dynamic tests, thus allowing us to further understand the insights of passive properties of the human hand joints. The viscoelastic behavior of the MCP joint showed a nonlinear dependency on the frequency. It implies that the elastic and viscous component of the hand joint coordinate to adapt to the external loading based on the applied frequency. The findings derived from the experiments with the mechanism can provide important guidelines for design of humanlike compliance of the robotic hands.


1992 ◽  
Vol 1 (1) ◽  
pp. 63-79 ◽  
Author(s):  
Thomas H. Speeter

Manipulation by teleoperation (telemanipulation) offers an apparently straightforward and less computationally expensive route toward dextrous robotic manipulation than automated control of multifingered robotic hands. The functional transformation of human hand motions into equivalent robotic hand motions, however, presents both conceptual and analytical problems. This paper reviews and proposes algorithmic methods for transforming the actions of human hands into equivalent actions of slave multifingered robotic hands. Forward positional transformation is considered only, the design of master devices, feedforward dynamics, and force feedback are not considered although their importance for successful telemanipulation is understood. Linear, nonlinear, and functional mappings are discussed along with performance and computational considerations.


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