Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier
Keywords:Fingerprint, Fingervein, Image Enhancement, Retinex, Stacked Auto Encoder, Random Forest, Equal Error Rate.
In recent years, the need for security of personal data is becoming progressively important. A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. With respect to this concern, the identification system based on the fusion of multibiometric values is the most recommended in order to significantly improve and obtain high performance accuracy. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Retinex Algorithm, Stacked Deep Auto Encoder and Random forest (RF) classifier based on multi-biometric fingerprint as well as finger-vein recognition system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. In order to gain above mentioned objectives, fingerprint and fingervein dataset is taken for training and testing. The result analysis shows approx. 97% accuracy, 92% precision rate as well as 0.04 EER that shows enhancement over existing work.
 Li Xueyan and Guo Shuxu,The Fourth Biometric - Vein Recognition , Jilin University, P. R. China Pattern Recognition Techniques, Technology and Applications.
 Jiman Kim,Hyoun-Joong Kong, Sangyun Park, Seung Woo Noh, Seung-Rae Lee, Taejeong Kim and Hee Chan Kim, Non contact Finger Vein Acquisition system using NIR Laser, Proc. SPIE 7249, Sensors, Cameras, and Systems for Industrial/Scientific Applications January 27, 2009.
 R. Das, E. Piciucco, E. Maiorana and P. Campisi, "Convolutional Neural Network for Finger-Vein-Based Biometric Identification," in IEEE Transactions on Information Forensics and Security, vol. 14, no. 2, pp. 360-373, Feb. 2019.
 Cihui Xi, Ajay Kumar, “Finger vein identification using Convolutional Neural Network and supervised discrete hashing”, Pattern Recognition Letters, Volume 119, 1 March 2019, pp. 148-156.
 B. Wenxuan, F. Zhihong, P. Min and W. Pu, "Research on Indoor Edge Location Based on Location Fingerprint Recognition," International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Qiqihar, China, 2019, pp. 302-306.
 O. I. Abiodun et al., "Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition," in IEEE Access, vol. 7, pp. 158820-158846, 2019.
 P. B. S. Serafim, "A Method based on Convolutional Neural Networks for Fingerprint Segmentation," International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, 2019, pp. 1-8.
 R. Raghavendra, S. Venkatesh, K. Raja and C. Busch, "A low-cost Multi-Fingervein Verification System," 2018 IEEE International Conference on Imaging Systems and Techniques (IST), Krakow, 2018, pp. 1-6.
 V. Krivokuca and S. Marcel, "Towards quantifying the entropy of fingervein patterns across different feature extractors," 2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA), Singapore, 2018, pp. 1-8.
 Subba Reddy Borra, G. Jagadeeswar Reddy, E. Sreenivasa Reddy, “Classification of fingerprint images with the aid of morphological operation and AGNN classifier”, Applied Computing and Informatics, vol. 14, Issue 2, 2018, pp. 166-176.
 K. Han, Z. Wang and Z. Chen, "Fingerprint Image Enhancement Method based on Adaptive Median Filter," 2018 24th Asia-Pacific Conference on Communications (APCC), Ningbo, China, 2018, pp. 40-44.
 Z. Rezaei and G. Abaei, "A Robust Fingerprint Recognition System Based on Hybrid DCT and DWT," 2017 24th National and 2nd International Iranian Conference on Biomedical Engineering (ICBME), Tehran, 2017, pp. 330-333.
 Lu yang, gongping yang, xiaoming xi, xianjing meng, Chunyun zhang, and yilong yin, “Tri-Branch Vein Structure Assisted Finger Vein Recognition”, IEEE Access, 2017.
How to Cite
Copyright (c) 2020 Amreen Khan, Dr. Abhishek Bhatt
This work is licensed under a Creative Commons Attribution 4.0 International License.
IJOSCIENCE follows an Open Journal Access policy. Authors retain the copyright of the original work and grant the rights of publication to the publisher with the work simultaneously licensed under a Creative Commons CC BY License that allows others to distribute, remix, adapt, and build upon your work, even commercially, as long as they credit you for the original creation. Authors are permitted to post their work in institutional repositories, social media or other platforms.
Under the following terms:
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.