Abstract
Parallel fusion is a promising fusion method in field of feature level fusion. Unlike conventional 2-feature parallel fusion with complex representation, it is tough to represent 4-feature parallel fusion due to shortage of mathematical significance. To solve this problem, this paper proposes a reasonable interpretation by introducing quaternion. Initially, this paper defines a novel ROI extraction method for obtaining more information from middle finger images. After parallel fusion whose features are extracted by 2D wavelets decomposition coefficients from the same pixel corresponding to 4 separate sub-images, Quaternion Euclidean Product Distance (QEPD), a distance between modulus square of template quaternion and modulus of tester Quaternion Euclidean Product (QEP), as matching score, is performed. The scores discriminate between genuine and impostor of finger texture by threshold effectively. Finally, the experimental result gains a reasonable recognition rate but at a fast speed. Through a comparison with palmprint QEPD, the recognition performance of this finger texture QEPD outperforms than palmprint.
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Qiu, D.L.Zd., Sun, Dm. (2009). Wavelet Decomposition Feature Parallel Fusion by Quaternion Euclidean Product Distance Matching Score for Finger Texture Verification. In: Kim, Th., et al. Advances in Computational Science and Engineering. FGCN 2008. Communications in Computer and Information Science, vol 28. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10238-7_12
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DOI: https://doi.org/10.1007/978-3-642-10238-7_12
Publisher Name: Springer, Berlin, Heidelberg
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