Identifying FKP-based Individuals Using the Feature Extraction of the Relaxed Local Ternary Patterns

Document Type: Original Article

Authors

1 Department of Electrical Engineering, Tafresh University, Tafrsh, Iran

2 School of Computing and Information Sciences, Florida International University.

10.22034/jbr.2020.232978.1024

Abstract

Identification based on biometric parameters is an effective way to identify people. The fingerprint effect is a feature with small image dimensions, and at the same time, distinguishable features in low image resolution and is used as a reliable biometric identifier. In this paper, a new method for identifying FKP-based individuals using the extraction feature of the Relaxed Local Ternary Patterns (RLTP) is suggested. The RLTP method has been proposed to identify faces and has led to favorable results. In this method, large neighborhood differences that are immune to noise are encoded in two specific states, and small neighborhood disturbances that are vulnerable to noise are encoded in an uncertain state. The chi-square distance criterion is used to calculate the similarity between the extraction features of the input and reference FKP images. The advantage of this method is low computational complexity while improving the high accuracy of recognition. Experimental results on a standard database confirm the success of the proposed method.

Graphical Abstract

Identifying FKP-based Individuals Using the Feature Extraction of the Relaxed Local Ternary Patterns

Keywords


References

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