Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier

Authors

  • Amreen Khan M.Tech. Scholar, Department of ECE, Technocrats Group of Institutions -A Bhopal, India
  • Dr. Abhishek Bhatt Professor, Department of ECE, Technocrats Group of Institutions -A, Bhopal, India

DOI:

https://doi.org/10.24113/ijoscience.v6i2.266

Keywords:

Fingerprint, Fingervein, Image Enhancement, Retinex, Stacked Auto Encoder, Random Forest, Equal Error Rate.

Abstract

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.

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References

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Published

02/10/2020

How to Cite

Khan, A., & Bhatt, D. A. (2020). Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier. SMART MOVES JOURNAL IJOSCIENCE, 6(2), 20–26. https://doi.org/10.24113/ijoscience.v6i2.266