Browse > Article
http://dx.doi.org/10.14400/JDC.2014.12.4.319

Hand Biometric Information Recognition System of Mobile Phone Image for Mobile Security  

Hong, Kyungho (Div. of Information and Communication Engineering, Baekseok University)
Jung, Eunhwa (Div. of Information and Communication Engineering, Baekseok University)
Publication Information
Journal of Digital Convergence / v.12, no.4, 2014 , pp. 319-326 More about this Journal
Abstract
According to the increasing mobile security users who have experienced authentication failure by forgetting passwords, user names, or a response to a knowledge-based question have preference for biological information such as hand geometry, fingerprints, voice in personal identification and authentication. Therefore biometric verification of personal identification and authentication for mobile security provides assurance to both the customer and the seller in the internet. Our study focuses on human hand biometric information recognition system for personal identification and personal Authentication, including its shape, palm features and the lengths and widths of the fingers taken from mobile phone photographs such as iPhone4 and galaxy s2. Our hand biometric information recognition system consists of six steps processing: image acquisition, preprocessing, removing noises, extracting standard hand feature extraction, individual feature pattern extraction, hand biometric information recognition for personal identification and authentication from input images. The validity of the proposed system from mobile phone image is demonstrated through 93.5% of the sucessful recognition rate for 250 experimental data of hand shape images and palm information images from 50 subjects.
Keywords
Mobile Security; Biometric Recognition System; Hand Biometric Information; Hand Shape Recognition; Palm Information;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Nok Nok Lab, "Moving Beyond Passwords Consumer Attitudes On Online Authentication", Ponemon Institute, April 2013
2 C. Oden, A. Ercil and B. Buke, "Combining implicit polynomials and geometric features for hand recognition," Pattern Recognit Lett.,Vol. 24, pp.2145-2152, 2003   DOI   ScienceOn
3 Yoruk, E. Konukoglu, E. Sankur and B. Darbon, J., "Shape-based hand recognition," IEEE transactions on image processing, Vol.15, pp.1803-1815, 2006   DOI   ScienceOn
4 Sanchez-Reillo, C. Sanchez-Avila and A. Gonzalez-Marcos, "Biometric identification through hand geometry measurement," IEEE Trans. Pattern Anal. Mach. Intell.,Vol.22, p.1168-1171, 2000   DOI   ScienceOn
5 Jure Kovac, Peter Peer and Franc Solina, "Human Skin Color Clustering for Face Detection," International Conference on Computer as a Tool., 2003
6 Ralph Gross, Sweeney, Yiheng Li, Xiaoquian Jiang, Wanhong Xu, Latanya Daniel Yurovsky, "Robust Hand Geometry Measurements for Personal Identification using Active Appearance Models", Carnegie Mellon University, School of Computer Science Technical Report CMU-ISRI-06-123, 2006
7 R. Gross, S. Baker, I. Matthews, and T. Kanade, "Face recognition across pose and illumination", in handbook of Face Recognition, S. Z. Li and A. K. Jain, Eds. Springer, pp 193-216, 2005
8 N. Tanibata, N. Shimada, "Extraction of Hand Features for Recognition of Sign Language Words", The 15th International Conference on Vision Interface, pp391-398, 2002.
9 A. Licsar, T. Sziranvi, "User-Adaptive Hand Gesture Recognition System with Interactive Training", Image and Vision Computing, Vol.23, No.12, pp.1102-1114, 2005.   DOI   ScienceOn