A new approach to hand-based authentication

被引:0
|
作者
Amayeh, G. [1 ]
Bebis, G. [1 ]
Erol, A. [1 ]
Nicolescu, M. [1 ]
机构
[1] Univ Nevada, Comp Vis Lab, Reno, NV 89557 USA
来源
BIOMETRIC TECHNOLOGY FOR HUMAN IDENTIFICATION IV | 2007年 / 6539卷
关键词
biometrics; hand-based authentication; Zernike moments;
D O I
10.1117/12.721032
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Hand-based authentication is a key biometric technology with a wide range of potential applications both in industry and government. Traditionally, hand-based authentication is performed by extracting information from the whole hand. To account for hand and finger motion, guidance pegs are employed to fix the position and orientation of the hand. In this paper, we consider a component-based approach to hand-based verification. Our objective is to investigate the discrimination power of different parts of the hand in order to develop a simpler, faster, and possibly more accurate and robust verification system. Specifically, we propose a new approach which decomposes the hand in different regions, corresponding to the fingers and the back of the palm, and performs verification using information from certain parts of the hand only. Our approach operates on 2D images acquired by placing the hand on a flat lighting table. Using a part-based representation of the hand allows the system to compensate for hand and finger motion without using any guidance pegs. To decompose the hand in different regions, we use a robust methodology based on morphological operators which does not require detecting any landmark points on the hand. To capture the geometry of the back of the palm and the fingers in sufficient detail, we employ high-order Zernike moments which are computed using an efficient methodology. The proposed approach has been evaluated on a database of 100 subjects with 10 images per subject, illustrating promising performance. Comparisons with related approaches using the whole hand for verification illustrate the superiority of the proposed approach. Moreover, qualitative comparisons with state-of-the-art approaches indicate that the proposed approach has comparable or better performance.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Designing an accurate hand biometric based authentication system fusing finger knuckleprint and palmprint
    Nigam, Aditya
    Gupta, Phalguni
    NEUROCOMPUTING, 2015, 151 : 1120 - 1132
  • [22] Mobile Authentication over Hand-Waving
    Filina, Alina N.
    Kogos, Konstantin G.
    PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE QUALITY MANAGEMENT,TRANSPORT AND INFORMATION SECURITY, INFORMATION TECHNOLOGIES (IT&QM&IS), 2017, : 69 - 74
  • [23] Robustness of Rhythmic-Based Dynamic Hand Gesture with Surface Electromyography (sEMG) for Authentication
    Wong, Alex Ming Hui
    Furukawa, Masahiro
    Maeda, Taro
    ELECTRONICS, 2020, 9 (12) : 1 - 16
  • [24] Keystroke dynamics based biometric authentication: A hybrid classifier approach
    Dwivedi, Chaitanya
    Kalra, Divyanshu
    Naidu, Divesh
    Aggarwal, Swati
    2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI), 2018, : 266 - 273
  • [25] Continuous User Authentication System: A Risk Analysis Based Approach
    Neha
    Chatterjee, Kakali
    WIRELESS PERSONAL COMMUNICATIONS, 2019, 108 (01) : 281 - 295
  • [26] Continuous User Authentication System: A Risk Analysis Based Approach
    Kakali Neha
    Wireless Personal Communications, 2019, 108 : 281 - 295
  • [27] An Approach to Developing EEG-Based Person Authentication System
    Patel, Meetkumar J.
    Husain, Mohammad, I
    2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, : 2619 - 2625
  • [28] A machine learning approach to keystroke dynamics based user authentication
    Revett, Kenneth
    Gorunescu, Florin
    Gorunescu, Marina
    Ene, Marius
    de Magalhaes, Sergio Tenreiro
    Dinis Santos, Henrique M.
    INTERNATIONAL JOURNAL OF ELECTRONIC SECURITY AND DIGITAL FORENSICS, 2007, 1 (01) : 55 - 70
  • [29] Personal Authentication and Hand Motion Recognition based on Wrist EMG Analysis by a Convolutional Neural Network
    Shioji, Ryohei
    Ito, Shin-ichi
    Ito, Momoyo
    Fukumi, Minoru
    2018 JOINT 10TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS (SCIS) AND 19TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS (ISIS), 2018, : 1172 - 1176
  • [30] Personal Authentication and Hand Motion Recognition based on Wrist EMG Analysis by a Convolutional Neural Network
    Shioji, Ryohei
    Ito, Shin-ichi
    Ito, Momoyo
    Fukumi, Minoru
    2018 IEEE INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND INTELLIGENCE SYSTEM (IOTAIS), 2018, : 184 - 188