Fusion of Finger Inner Knuckle Print and Hand Geometry Features to Enhance the Performance of Biometric Verification System

With the advent of modern computing technology, there is an increased demand for developing recognition systems that have the capability of verifying the identity of individuals. Recognition systems are required by several civilian and commercial applications for providing access to secured resources. Traditional recognition systems which are based on physical identities are not sufficiently reliable to satisfy the security requirements due to the use of several advances of forgery and identity impersonation methods. Recognizing individuals based on his/her unique physiological characteristics known as biometric traits is a reliable technique, since these traits are not transferable and they cannot be stolen or lost. Since the performance of biometric based recognition system depends on the particular trait that is utilized, the present work proposes a fusion approach which combines Inner knuckle print (IKP) trait of the middle, ring and index fingers with the geometrical features of hand. The hand image captured from a digital camera is preprocessed to find finger IKP as region of interest (ROI) and hand geometry features. Geometrical features are represented as the distances between different key points and IKP features are extracted by applying local binary pattern descriptor on the IKP ROI. The decision level AND fusion was adopted, which has shown improvement in performance of the combined scheme. The proposed approach is tested on the database collected at our institute. Proposed approach is of significance since both hand geometry and IKP features can be extracted from the palm region of the hand. The fusion of these features yields a false acceptance rate of 0.75%, false rejection rate of 0.86% for verification tests conducted, which is less when compared to the results obtained using individual traits. The results obtained confirm the usefulness of proposed approach and suitability of the selected features for developing biometric based recognition system based on features from palmar region of hand.

• M. L. Anitha
• K. A. Radhakrishna Rao

• Biometrics
• hand geometry features
• inner knuckle print
• recognition.

[1] Anil K Jain, “An Introduction to Biometric Recognition”, IEEE transactions on circuits and systems for video technology, vol. 14, no. 1, pp 1-20, January 2004.
[2] Kresimir Delac, Mislav Grgic, “A Survey of Biometric Recognition methods”, 46th International Symposium of Electronics in Marine, pp. 184-193, June 2004.
[3] Arun A Ross, Anil K Jain, “Multimodal Biometrics an overview”, Proceedings of 12th European Signal Processing Conference, Poland, pp. 1121-1124, September 2007.
[4] Arun A Ross, Karthik Nandakumar, Anil K Jain, "Handbook of Multibiometrics", Springer 2006.
[5] Rahman, A., Anwar, F. & Azad, S.," A Simple and Effective Technique for Human Verification with Hand Geometry", International Conference on Computer and Communication Engineering, pp. 1177–1180, 2008.
[6] Vivek Kanhangad, Ajay Kumar and David Zhang, “Contactless and Pose Invariant Biometric Identification using Hand Surface”, IEEE Transactions on Image Processing, vol. 20, no. 5, pp.1415-1424, May 2011.
[7] Qiang Li, Zhengding Qiu, Dongmei Sun, Jie Wu, “Personal Identification Using Knuckleprint” Advances in Biometric Person Authentication, 5th Chinese Conference on Biometric recognition, Sinobiometrics 2004 Proceedings, pp 680-689.
[8] Ribaric, S., & Fratric, I., “A biometric identification system based on eigenpalm and eigenfinger features”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 27 , Issue 11, Nov. 2005, pp 1698 – 1709.
[9] Nanni, L. & Lumini, “A multi-matcher system based on knuckle-based features”, Neural computing and applications, January 2009, Volume 18, Issue 1, pp 87–91.
[10] Goh Kah Ong Michael, Tee Connie, Andrew Teoh Beng Jin, “An innovative contactless palm print and knuckle print recognition system”, Pattern Recognition Letters, 31, 2010, pp 1708–1719.
[11] Xuemiao Xu, Qiang Jin, Le Zhou, Jing Qin, Tien-Tsin wong, Guoqiang Han, "Illumunation- Inavrient and Deformation-Tolerant Inner Knuckle Print Recognition Using Portable Devices", No 15, pp 4326-4352, Sensors 2015.
[12] PolyUFinger knuckle print database, Available at http://www.Comp.ployu.edu.hk/biometrics. Accessed on 25th July 2015.
[13] M L Anitha and K A Radha Krishna Rao, "Extraction of Region of Interest (ROI) for Palm Print and Inner Knuckle Print", International Journal of Computer Applications (0975 – 8887) Volume 124 – No.14, August 2015.
[14] T. Ojala, M. Pietikainen and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”, IEEE transaction on Pattern Analysis and Machine Intelligence (PAMI), vol. 27(7), pp 971-987, 2002.