Multimodal Biometric Authentication System Based on Score-Level Fusion of Palmprint and Finger Vein
Multimodal biometrics plays a major role in our day-to-day life to meet the requirements with the well-grown population. In this paper, palmprint and finger vein images are fused using normalization scores of the individual traits. Palmprint features extracted from the discrete cosine transform (DCT) are classified by using multi-class linear discriminant analysis (LDA) and self-organizing maps (SOM). Finger vein identification is designed and developed by using repeated line tracking method to extract the patterns. A multimodal biometric authentication system integrates information from multiple biometric sources to compensate for the limitations in performance of each individual biometric system. These systems can significantly improve the recognition performance of a biometric system apart from catalyzing population coverage, impeding spoof attacks, increasing the degrees of freedom, and reducing the failure rates.
KeywordsMultimodal biometrics DCT Multi-class LDA SOM
In recent years, multimodal biometrics has gained the substantial attention to all organizations and more than one biometric is fused together. Vein patterns serve a highly secured authentication system over other biometrics. It is noninvasive, reliable, and well accepted by users . Preprocessing of finger vein images yields a better quality image by removing the noise and increasing the image contrast . Acquisition of infrared finger vein image using various LED contains not only the vein patterns but also irregular shading produced by the various thicknesses of the finger bones and muscles . The finger vein pattern from the unclear images is extracted by using line tracking, which starts from various positions. A person retrieval solution using finger vein can be accomplished by searching an image in the database in a reasonable time . A wide line detector for feature extraction can obtain precise width information of the finger vein and improve the inferences of the extracted feature from low-quality image . The finger vein patterns extracted by using gradient-based threshold and maximum curvature points are applied to neural network to train and test the quality of system [6, 7]. Extracting the finger vein patterns regardless of vein thickness or brightness is necessary for accurate personal identification . Face and finger vein biometric authentication system at multi-level score-level fusion is very efficient to reduce the false rejection rate . Multiple features like texture (gabor), line, and appearance (PCA) features extracted from the palmprint images are fused using particle swarm optimization techniques to improve the performance [10, 11, 12]. The wavelet-based fusion technique is suggested to fuse extracted features as it contains wavelet extensions and uses mean–max fusion method to overcome the problem of feature fusion .
The rest of this paper is organized as follows: Sect. 2 describes the palmprint recognition system. Section 3 highlights the feature extraction algorithm for finger vein authentication system. Score-level fusion is discussed in Sect. 4. Section 5 provides experimental results of the proposed system, and Sect. 6 offers the conclusion.
2 Palmprint Authentication
This paper designs the most efficient, high-speed method for palmprint recognition and also develops an algorithm for the palmprint recognition system which formulates an image-based approach, using the 2-dimensional discrete cosine transform (2D-DCT) for image compression and a combination of multi-class linear discriminant analysis (LDA) and self-organizing map (SOM) neural network  for recognition purpose.
2.1 Image Compression
The image compressed using 2D blocked discrete cosine transform (DCT) is applied with a mask, and high coefficients in the image are discarded.
For n1 = 0, 1… N1 − 1 and n2 = 0, 1… N2 − 1
2.2 Multi-class LDA
2.3 Self-Organizing Feature Maps
Thus, units close to the winners as well as the winners themselves have their weights updated appreciably.
3 Finger Vein Authentication
The patterns of veins were extracted by combining two segmentation methods, which include morphological operation and maximum curvature points in image profiles. The finger vein patterns were acquired by passing near-infrared light through the finger vein. The result is an image of the unique patterns of veins, as dark lines can be captured by a sensor placed below the finger.
3.1 Feature Extraction and Matching
4 Score-Level Fusion
The matching scores of palmprint recognition is obtained by finding the minimum absolute deviation for each palmprint image. The matching scores of finger vein recognition are normalized using Z-score normalization technique. The mean (μ) and standard deviation (σ) are estimated from a given set of matching scores.
5 Experimental Results and Discussions
Absolute minimum deviation of palmprint images
No. of subjects
Normalization scores of finger vein images
No. of subjects/samples
Recognition performance of the proposed system
Recognition Rate %
Palmprint + Finger vein
The proposed method bypasses the need to perform score normalization and choosing optimal combination weights for each modality. In this sense, the proposed solution is a principled and general approach that is optimal when the matching score distributions are either known or can be estimated with high accuracy. Palmprint authentication is implemented using LDA and SOM for feature classification and 2D-DCT for image compression. Finger vein is authenticated by using repeated line tracking for feature extraction, and matching is done with the template created. Once identification is done, minimum deviation for palmprint and matching score for finger vein are calculated and are fused using scoring level. Error rate is reduced, and it provides accurate results.
- 1.X. Li, S. Guo, The fourth biometric—vein recognition. Pattern Recogn. Tech. Technolo. Appl. 24, 626 (2008)Google Scholar
- 2.D. Hejtmankova, R. Dvorak, A new method of finger veins detection. Int. J. Bio-Sci. Bio-Technol. 1, 11 (2009)Google Scholar
- 4.N. Miura, T. Miyataket, A. Nagasakat, Automatic feature extraction from non-uniform finger vein image and its application to personal identification. IAPR Worshop on Machine Vision Applications. (2002)Google Scholar
- 5.B. Huang, Y. Dai, R. Li, D. Tang, W. Li, Finger-vein authentication based on wide line detector and pattern normalization. International Conference on Pattern Recognition. (2010), pp. 1269–1273Google Scholar
- 6.I. Malik, R. Sharma, Analysis of different techniques for finger-vein feature extraction. Int. J. Comput. Trends Technol. (IJCTT). 4(5) (2013)Google Scholar
- 7.A.N. Hoshyar, R. Sulaiman, A.N. Houshyar, Smart access control with finger vein authentication and neural network. J. Am. Sci. 7(9) (2011)Google Scholar
- 8.J.H. Choi, W. Songa, T. Kima, S.-R. Leeab, H.C. Kim, Finger vein extraction using gradient normalization and principal curvature. Proceedings of SPIE, Image Processing: Machine Vision Applications II. (2009), pp. 7251Google Scholar
- 10.G. Yang, X. Xi, Y. Yin, Finger vein recognition based on (2D)2 PCA and metric learnin. J. Biomed. Biotechnol. 2, (2012)Google Scholar
- 11.K. Krishneswari, S. Arumugam Intramodal feature fusion based on PSO for palmprint authentication. ICTACT J. Image Video Process. 02(04) (2012)Google Scholar
- 12.P. Tamil Selvi, N. Radha, Palmprint and Iris based authentication and secure key exchange against dictionary attacks. Int. J. Comput. Appl. 2 (11) (2010)Google Scholar
- 13.K. Krishneswari, S. Arumugam, Intramodal feature fusion using wavelet for palmprint authentication, Int. J. Eng. Sci. Technol. 3(2011)Google Scholar
- 15.T. Connie, A. Jin, M. Ong, D. Ling, An automated palmprint recognition system. Image Vision Comput. 23 (2005)Google Scholar
- 17.A. Hussein A. Al-Timemy, A robust algorithm for ear recognition system based on self organization maps. 1st Regional Conference of Engineering Science NUCEJ (special issue). 11(2) (2008)Google Scholar