Double stages of feature extarction-based GFPMI for colored finger vein identification

Dawlat Mustafa Sulaiman, Adnan Mohsin Abdulazeez, Habibollah Haron


Today, finger vein recognition has a lot of attention as a promising approach of biometric identification framework and still does not meet the challenges of the researchers on this filed. To solve this problem, we propose s double stage of feature extraction schemes based localized finger fine image detection. We propose Globalized Features Pattern Map Indication (GFPMI) to extract the globalized finger vein line features basede on using two generated vein image datasets: original gray level color, globalized finger vein line feature, original localized gray level image, and the colored localized finger vein images. Then, two kinds of features (gray scale and texture features) are extracted, which tell the structure information of the whole finger vein pattern in the whole dataset. The recurrent based residual neural network (RNN) is used to identify the finger vein images. The experimental show that the localized colored finger vein images based globalized feature extraction has achieved the higher accuracy (93.49%) while the original image dataset achieved less accuracy by (69.86%).


Classification; Finger vein; Glcm features; Gray scale texture features; Identification; Localization; Residual neural network

Full Text:



Sravya., V, Radha, K., Ravindra, B., and Srujana, B, "A Survey on Fingerprint Biometric System", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, Issue 4, April 2012.

Haar, Helen, Darelle van Greunen, and Dalenca Pottas, "The characteristics of a biometric", IEEE In Information Security for South Africa 2013.: 1-8.

Malik, Iram, and Rohini Sharma, "Analysis of different techniques for finger-vein feature extraction", Int. J. Comput. Trends Technol (IJCTT) 5: 4, 2013.

Kono, Miyuki, Naoto Miura, Takao Fujii, Koichiro Ohmura, Hajime Yoshifuji, Naoichiro Yukawa, Yoshitaka Imura et al. "Personal Authentication Analysis Using Finger-Vein Patterns in Patients with Connective Tissue Diseases—Possible Association with Vascular Disease and Seasonal Change." PloS one 10, no. 12 (2015): e0144952.

Yang, Jinfeng, and Yihua Shi. "Towards finger-vein image restoration and enhancement for finger-vein recognition." Information Sciences 268 (2014): 33-52.

Bonneau, Joseph, and Sören Preibusch. "The Password Thicket: Technical and Market Failures in Human Authentication on the Web." WEIS. 2010.

Mayyadah R. Mahmood, Adnan M. Abdulazeez, “A Comparative Study of a New Hand Recognition Model Based on Line of Features and Other Techniques”, nternational Conference of Reliable Information, 2017.

Mayyadah Ramiz Mahmood, Adnan Mohsin Abdulazeez,” Different Model for Hand Gesture Recognition with a Novel Line Feature Extraction”, 2019 International Conference on Advanced Science and Engineering (ICOASE).

Dawlat Mustafa Sulaiman, Adnan Mohsin Abdulazeez, Habibollah Haron, Shereen S Sadiq,” Unsupervised Learning Approach-Based New Optimization K-Means Clustering for Finger Vein Image Localization”, 2019 International Conference on Advanced Science and Engineering (ICOASE).

R. Khanam, R. Khan and R. Ranjan, "Analysis of Finger Vein Feature Extraction and Recognition using DA and KNN Methods," 2019 Amity International Conference on Artificial Intelligence (AICAI), Dubai, United Arab Emirates,2019,pp.477-483.doi: 10.1109/AICAI.2019.8701253

Y. Lu, S. Xie and S. Wu, "Exploring Competitive Features Using Deep Convolutional Neural Network for Finger VeinRecognition,"in IEEEAccess,vol.7,pp.35113-35123,2019.

doi: 10.1109/ACCESS.2019.2902429

Manmohan and V. Sharma, "Finger Vein Recognition Using Modified Maximum Edge Position Octal Patterns," 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN), Noida, India, 2019,pp.287-292.doi: 10.1109/SPIN.2019.8711573.

Wikipedia.n.d.Normalization (image processing).

Bob HowisonJune,“Bob’s Imaging Fundamentals #3: Pseudocolor and LUTs”, 18, 2015,

Shweta Jain, "Brain Cancer Classification Using GLCM Based Feature Extraction in Artificial Neural Network", IJCSET, ISSN: 2229-3345, VoL 4 No. 07 JuI2013.


Anja Attig, Petra Perner,” A Comparison between Haralick´s Texture Descriptor and the Texture Descriptor Based on Random Sets for Biological Images”, Machine Learning and Data Mining in Pattern Recognition. Volume 6871 of the series Lecture Notes in Computer Science pp 524-538.

Leyton, M.: Symmetry, Causality, Mind. MIT Press, Massachusetts (1992) .

Churchland, P., Sejnowski, T.: The Computational Brain, MIT Press (1992).

Yilong Yin, Lili Liu, and Xiwei Sun. n.d. "SDUMLA-HMT: A Multimodal Biometric Database." School of Computer Science and Technology, Shandong University.

D. Al-Jumeily, A. Al-Azzawi, A. Mahdi and J. Hind. 2017. "A Robust Spatially Invariant Model for Latent Fingerprint Authentication Approach." 2017 10th International Conference on Developments in eSystems Engineering (DeSE). Paris.

Adil Al-Azzawi, Hasanain Al-Sadr, Jianlin Cheng, Tony X Han,” Localized Deep Norm-CNN Structure for Face Verification”, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

Adil Al-Azzawi, Jade Hind, Jianlin Cheng,” Localized Deep-CNN Structure for Face Recognition”, 2018 11th International Conference on Developments in eSystems Engineering (DeSE).

Zeebaree, D. Q., Haron, H., & Abdulazeez, A. M. (2018, October). Gene Selection and Classification of Microarray Data Using Convolutional Neural Network. In 2018 International Conference on Advanced Science and Engineering (ICOASE) (pp. 145-150). IEEE.

Zeebaree, D. Q., Haron, H., Abdulazeez, A. M., & Zebari, D. A. (2019, April). Trainable Model Based on New Uniform LBP Feature to Identify the Risk of the Breast Cancer. In 2019 International Conference on Advanced Science and Engineering (ICOASE) (pp. 106-111). IEEE.

Borra, S. R., Reddy, G. J., & Reddy, E. S. (2018). An Efficient Fingerprint Identification using Neural Network and BAT Algorithm. International Journal of Electrical & Computer Engineering (2088-8708), 8(2).

Total views : 50 times


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

shopify stats IJEECS visitor statistics