Home / Current Issue / Paper 1703654
Development of Palmvein Recognition System Using Fire Fly Algorithm
Subject area: Science,Engineering and Technology · Area of research: Biometric
Abstract
Palmvein technology is one of the most popular fields in pattern recognition. The most distinguishing advantage of vein features are high level of accuracy, difficult to forge and more table features. In this study, palmvein images of individuals were acquired; a Linear Discriminant Analysis and Firefly Algorithm (LDA-FA) model for feature extraction was formulated and implemented and the performance of the developed system was benchmarked with the LDA model. Five (5) palmvein images of each one hundred (100) individuals were captured using an infrared CCD sensitive camera. Linear discriminant Analysis was enhanced with Firefly Algorithm to extract sufficient features. Back Propagation Neural Network (BPNN) was used to determine the class the training and the testing image belong. 270 images were used in training the database and 230 images were used for testing the created database. The system was tested using False Positive Rate, False Negative Rate, Recognition Accuracy and Average Recognition Time. The system was tested for False Positive Rate, False Negative Rate and accuracy at threshold values of 0.25, 0.46, 0.60, 0.85. The LDA-FA achieved a false positive rate of 18.00%, 10.00%, 6.00%, 2.00%, false negative rate of 1.11%, 2.22%, 2.78%, 3.33% and accuracy of 95.22%, 96.09%, 96.52% and 96.96% at the threshold values respectively. The LDA achieved a false positive rate of 22.00%, 14.00%, 10.00%, 4.00%, false negative rate of 4.44%, 5.00%, 5.56%, 6.67% and accuracy of 91.74%, 93.04%, 93.48% and 93.91% at the threshold values respectively. The average training time generated by LDA-FA are 200.32s, 199.87s, 201.94 and 202.91 while that of LDA are 219.76s, 219.93s, 220.38s and 220.71 at the threshold values respectively. The result shows that the LDA-FA is less computationally expensive in terms of training time compared to the LDA model. The study concluded that the LDA-FA is more accurate with minimal false positive and false negative than LDA.
Keywords
Palmvein Technology, Recognition System, Firefly Algorithm.
References
[1] Adams Kong, David Zhang, Mohamed Kamel (2009): A Survey of Palmprint Recognition, Journal of Pattern Recognition 42, Pp. 1408-1418
[2] Akhtar, Z., Hadid, A., Nixon, M., Tistarelli, M., Dugelay, J. L., & Marcel, S. (2017). Biometrics: In search of identity and security (Q & A). IEEE MultiMedia.
[3] Al-Ta’i, Z. T. M., & Abd Al-Hameed, O. Y. (2013). Comparison between PSO and firefly algorithms in fingerprint authentication. International Journal of Engineering and Innovative Technology (IJEIT), 3(1), 421-425.
[4] Alsultan, A., & Warwick, K. (2013). Keystroke dynamics authentication: a survey of free-text methods. International Journal of Computer Science Issues (IJCSI), 10(4), 1.
[5] Anila, S., & Devarajan, N. (2012). Preprocessing technique for face recognition applications under varying illumination conditions. Global Journal of Computer Science and Technology.
[6] Anjum, M. A. (2008). Improved face recognition using image resolution reduction and Optimization of Feature Vector (Doctoral dissertation, NATIONAL UNIVERSITY OF SCIENCES & TECHNOLOGY, PAKISTAN).
[7] Aykut, M., & Ekinci, M. (2013). AAM-based palm segmentation in unrestricted backgrounds and various postures for palmprint recognition. Pattern Recognition Letters, 34(9), 955-962.
[8] Basu, J. K., Bhattacharyya, D., & Kim, T. H. (2010). Use of artificial neural network in pattern recognition. International journal of software engineering and its applications, 4(2).
[9] Baudat, G., & Anouar, F. (2000). Generalized discriminant analysis using a kernel approach. Neural computation, 12(10), 2385-2404.
[10] Belhumeur, P. N., & Kriegman, D. J. (1998). What is the set of images of an object under all possible illumination conditions? International Journal of Computer Vision, 28(3), 245-260
[11] Chaudhury, S., & Roy, A. K. (2013). Histogram Equalization-A Simple but Efficient Technique for Image Enhancement. International Journal of Image, Graphics and Signal Processing, 5(10), 55.
[12] Chen, L. F., Liao, H. Y. M., Ko, M. T., Lin, J. C., & Yu, G. J. (2000). A New LDA-Based Face Recognition System which can Solve the Small Sample Size Problem. Pattern Recognition, 33(10), 1713-1726.
[13] Coventry, L., De Angeli, A., & Johnson, G. (2003). Usability and biometric verification at the ATM interface. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 153-160).
[14] Deepamalar, M., & Madheswaran, M. (2010). An improved multimodal palm vein recognition system using shape and texture features. International Journal of Computer Theory and Engineering, 2(3), 436.
[15] Deepamalar, M., & Madheswaran, M. (2010). An enhanced palm vein recognition system using multi-level fusion of multimodal features and adaptive resonance theory. International Journal of Computer Applications, 1(20), 95-101.
[16] Ding, Y., Zhuang, D., & Wang, K. (2005). A study of hand vein recognition method. In IEEE International Conference Mechatronics and Automation, 2005 (Vol. 4, pp. 2106-2110). IEEE.
[17] Doublet, J., Lepetit, O., & Revenu, M. (2007). Contact less Palmprint Authentication Using Circular Gabor Filter and Approximated String Matching.
[18] Duan, H. and Luo, Q. (2015). New progresses in swarm intelligence-based computation. International Journal of Bio-Inspired Computing, 7(10):26-35.
[19] Erdem Yoruk, Ender Komukoghi, Bulent Sankur & Jerome Darbon (2006): Shape Based Hand Recognition, IEEE Transaction on Image Processing, 15(7), 1803-1815.
[20] Fan, C. N., & Zhang, F. Y. (2011). Homomorphic Filtering Based Illumination Normalization Method for Face Recognition. Pattern Recognition Letters, 32(10), 1468-1479.
[21] García, R., Barrientos, R. J., Rojas, C., & Mora, M. (2019). Individuals Identification Based on Palmvein Matching under a Parallel Environment. Applied Sciences, 9(14), 2805.
[22] Gupta, S., & Gagneja, A. (2014). Proposed iris recognition algorithm through image acquisition technique. International Journal of Advanced Research in Computer Science and Software Engineering, 4(2).
[23] Hao Luo, Fe-Xin Yu, Jeng-Shyang, Shu-Chuan Chu and Pei-Wei Tsai (2010): “A Survey of Vein Recognition Techniques”, Information Technology Journal 9, Pp. 1142-1149, New York.
[24] Harrell, E., & Langton, L. (2015). Victims of identity theft, 2014. U.S. Department of Justice.
[25] Hernández-García, R., Barrientos, R. J., Rojas, C., & Mora, M. (2019). Individuals Identification Based on Palm Vein Matching under a Parallel Environment. Applied Sciences, 9(14), 2805.
[26] Jadhav, I. S., Gaikwad, V. T., & Patil, G. U. (2011). “Human Identification Using Voice and Face Recognition” International Journal of Computer Science and Information Technologies (IJCSIT), 2(3),1248-1252.
[27] Jain, A. K., Flynn, P., & Ross, A. A. (Eds.). (2007). Handbook of biometrics. Springer Science & Business Media.
[28] Jain, A. K., Chen, Y., & Demirkus, M. (2006). Pores and Ridges: High-Resolution Fingerprint Matching Using Level 3 Features. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1), 15-27.
[29] Jain, A. K., Ross, A., & Prabhakar, S. (2004). An Introduction to Biometric Recognition. IEEE Transactions on Circuits and Systems for Video Technology, 14(1), 4-20.
[30] Jain, A. K., Duin, R. P. W., & Mao, J. (2000). Statistical pattern recognition: A review. IEEE Transactions on pattern analysis and machine intelligence, 22(1), 4-37.
[31] Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Proceedingsof the IEEE International Conference on Neural Networks, 1942-1948. IEEE,Middlesex County, New Jersey.
[32] Khan, M. H. M., Subramanian, R. K., & Khan, N. M. (2009). Low dimensional representation of dorsal hand vein features using principle component analysis (PCA). World Academy of Science, Engineering and Technology, 49, 1001-1007.
[33] Kong, A., Zhang, D., & Kamel, M. (2009). A survey of palmprint recognition. pattern recognition, 42(7), 1408-1418.
[34] Kumar, A., Wong, D. C., Shen, H. C., & Jain, A. K. (2003). Personal Verification Using Palmprint and Hand Geometry Biometric. In International Conference on Audio-And Video-Based Biometric Person Authentication (Pp. 668-678). Springer, Berlin, Heidelberg.
[35] Lee, J. C. (2012). A novel biometric system based on palm vein image. Pattern Recognition Letters, 33(12), 1520-1528.
[36] Lin, X., Chao, H., Fan, J., & Diwan, S. (2013). U.S. Patent No. 8,391,593. Washington, DC: U.S. Patent and Trademark Office.
[37] Liu, Q., Huang, R., Lu, H., & Ma, S. (2002). Face recognition using kernel-based fisher discriminant analysis. In Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition (pp. 197-201). IEEE.
[38] Liu, Q., Lu, H., & Ma, S. (2004). Improving kernel Fisher discriminant analysis for face recognition. IEEE transactions on circuits and systems for video technology, 14(1), 42-49.
[39] Liu, Z., Yin, Y., Wang, H., Song, S., & Li, Q. (2010). Finger vein recognition with manifold learning. Journal of Network and Computer Applications, 33(3), 275-282.
[40] Lu, W., Li, M., & Zhang, L. (2016). Palmvein recognition using directional features derived from local binary patterns. International Journal of Signal Processing, Image Processing and Pattern Recognition, 9(5), 87-98
[41] Luo, H., Yu, F. X., Pan, J. S., Chu, S. C., & Tsai, P. W. (2010). A survey of vein recognition techniques. Information Technology Journal, 9(6), 1142-1149.
[42] Matthew Turk and Alex Pentland (1991): “Eigenfaces for Face Recognition”. Journal of Cognitive Neuroscience, Vol. 3, No.1, Pp. 71-86.
[43] Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2009). Fingerprint matching. Handbook of Fingerprint Recognition, 167-233.
[44] Mika, S., Ratsch, G., Weston, J., Scholkopf, B., & Mullers, K. R. (1999). Fisher discriminant analysis with kernels. In Neural networks for signal processing IX: Proceedings of the 1999 IEEE signal processing society workshop (cat. no. 98th8468) (pp. 41-48). IEEE.
[45] Miura .N, Nagasaka .A & Miyatake .T (2005). “Extraction of finger-vein patterns using maximum curvature points in image profiles based on repeated line tracking and its application to personal identification,”. Mach. Vis. Appl, 15(2).
[46] Muhammad Almas Anjum (2008): “Improved Face Recognition Using Image Resolution Reduction and Optimization of Feature Vector”, Ph. D Thesis, National University of Sciences and Technology Rawalpindi Pakistan.
[47] Nagar, A., Nandakumar, K., & Jain, A. K. (2010). A hybrid biometric cryptosystem for securing fingerprint minutiae templates. Pattern Recognition Letters, 31(8), 733-741.
[48] Nandakumar, K. (2008). Multibiometric systems: Fusion strategies and template security. MICHIGAN STATE UNIV EAST LANSING DEPT OF COMPUTER SCIENCE/ENGINEERING.
[49] Omidiora, E. O., Fakolujo, O. A., Ayeni, R. O., Olabiyisi, S. O., & Arulogun, O. T. (2008). Quantitative Evaluation of Principal Component Analysis and Fisher Discriminant Analysis Techniques in Face Images. Journal of Computer Science and its Application, (15), No.1
[50] Prince, S. J., & Elder, J. H. (2007). Probabilistic linear discriminant analysis for inferences about identity. In 2007 IEEE 11th International Conference on Computer Vision (pp. 1-8). IEEE.
[51] Ravi, D. (2006). An Introduction to biometric: A concise overview of the most important biometric technologies. Keesing Journal of Documents & Identity, 17.
[52] Razzak, A. A., & Hashem, A. R. (2015). Facial expression recognition using hybrid transform. International Journal of Computer Applications, 119(15).
[53] Rudrapal, D., Das, S., Debbarma, S., Kar, N., Debbarma, N., & Rudrapal, D. (2012). Voice recognition and authentication as a proficient biometric tool and its application in online exam for PH people. International Journal of Computer Applications, 39(12), 6-12.
[54] Sarkar, I., Alisherov, F., Kim, T. H., & Bhattacharyya, D. (2010). Palm vein authentication system: a review.”, International Journal of Control and Automation 3(1), 27-34.
[55] Singh, C. P., Jain, S., & Jain, A. (2014). Literature survey on fingerprint recognition using level 3 feature extraction method. International Journal of engineering and computer science, 3(1).
[56] Tanaka, T., & Kubo, N. (2004, August). Biometric authentication by hand vein patterns. In SICE 2004 Annual Conference (Vol. 1, pp. 249-253). IEEE.
[57] Tang, Q., Shen, Y., and Hu, C. (2013). Swarm Intelligence: Based Cooperation Optimization of Multi-Modal Functions. Cognitive Computing, 5(10):48-55.
[58] Tiwari, S., Chourasia, J. N., & Chourasia, V. S. (2015). A review of advancements in biometric systems. International Journal of Innovative Research in Advanced Engineering, 2(1), 187-204.
[59] Turk, M., & Pentland, A. (1991). Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1), 71-86.
[60] Vapnik, V. N. (1995). The nature of statistical learning. Theory.
[61] Wang, Y., Liu, T., & Jiang, J. (2008). A multi-resolution wavelet algorithm for hand vein pattern recognition. Chinese optics letters, 6(9), 657-660.
[62] Watanabe, M., Endoh, T., Shiohara, M., & Sasaki, S. (2005, September). Palm vein authentication technology and its applications. In Proceedings of the biometric consortium conference (pp. 19-21).
[63] Weaver A.C. (2006). “Biometric Authentication”, Computer, 39(2), 96-97.
[64] Wu, J. D., & Ye, S. H. (2009). Driver identification using finger-vein patterns with Radon transform and neural network. Expert Systems with Applications, 36(3), 5793-5799.
[65] Yambor, W. S. (2000). Analysis of PCA-based and Fisher discriminant-based image recognition algorithms (Master's thesis, Colorado State University).
[66] Yang, W., Wang, S., Hu, J., Zheng, G., Chaudhry, J., Adi, E., & Valli, C. (2018). Securing mobile healthcare data: a smart card based cancelable finger-vein bio-cryptosystem. IEEE Access, 6, 36939-36947.
[67] You, J., Li, W., & Zhang, D. (2002). Hierarchical palmprint identification via multiple feature extraction. Pattern recognition, 35(4), 847-859.
[68] Zhang, D., Kong, W. K., You, J., & Wong, M. (2003). Online palmprint identification. IEEE Transactions on pattern analysis and machine intelligence, 25(9), 1041-1050.
[69] Zhang, D., Jing, X., & Yang, J. (2006). Biometric image discrimination technologies. IGI Global.
[70] Zhang, Y. B., Li, Q., You, J., & Bhattacharya, P. (2007). Palm vein extraction and matching for personal authentication. In International Conference on Advances in Visual Information Systems (pp. 154-164). Springer, Berlin, Heidelberg
[71] Zhao, S., Wang, Y., & Wang, Y. (2007). Extracting hand vein patterns from low-quality images: a new biometric technique using low-cost devices. In Fourth International Conference on Image and Graphics (ICIG 2007) (pp. 667-671). IEEE.
How to cite this paper
@article{1703654,
author = {Famuyiwa, Kolawle Samuel. A, Mosud Y. Olumoye, Abisola Ayomide Olayiwola, Dawodu, Adekunle Alani},
title = {Development of Palmvein Recognition System Using Fire Fly Algorithm},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {1},
pages = {356-365},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703654.pdf},
abstract = {Palmvein technology is one of the most popular fields in pattern recognition. The most distinguishing advantage of vein features are high level of accuracy, difficult to forge and more table features. In this study, palmvein images of individuals were acquired; a Linear Discriminant Analysis and Firefly Algorithm (LDA-FA) model for feature extraction was formulated and implemented and the performance of the developed system was benchmarked with the LDA model. Five (5) palmvein images of each one hundred (100) individuals were captured using an infrared CCD sensitive camera. Linear discriminant Analysis was enhanced with Firefly Algorithm to extract sufficient features. Back Propagation Neural Network (BPNN) was used to determine the class the training and the testing image belong. 270 images were used in training the database and 230 images were used for testing the created database. The system was tested using False Positive Rate, False Negative Rate, Recognition Accuracy and Average Recognition Time. The system was tested for False Positive Rate, False Negative Rate and accuracy at threshold values of 0.25, 0.46, 0.60, 0.85. The LDA-FA achieved a false positive rate of 18.00%, 10.00%, 6.00%, 2.00%, false negative rate of 1.11%, 2.22%, 2.78%, 3.33% and accuracy of 95.22%, 96.09%, 96.52% and 96.96% at the threshold values respectively. The LDA achieved a false positive rate of 22.00%, 14.00%, 10.00%, 4.00%, false negative rate of 4.44%, 5.00%, 5.56%, 6.67% and accuracy of 91.74%, 93.04%, 93.48% and 93.91% at the threshold values respectively. The average training time generated by LDA-FA are 200.32s, 199.87s, 201.94 and 202.91 while that of LDA are 219.76s, 219.93s, 220.38s and 220.71 at the threshold values respectively. The result shows that the LDA-FA is less computationally expensive in terms of training time compared to the LDA model. The study concluded that the LDA-FA is more accurate with minimal false positive and false negative than LDA.},
keywords = {Palmvein Technology, Recognition System, Firefly Algorithm.},
month = {July},
}