Fingerprint Matching Based on Dimension Reduced DCT Feature Vectors


Sangita Bharkad, Manesh Kokare, Journal of Information Processing Systems Vol. 13, No. 4, pp. 852-862, Aug. 2017  

10.3745/JIPS.02.0017
Keywords: Biometric, Discrete Cosine Transform, Fingerprint Identification, Similarity Measure
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Abstract

In this work a Discrete Cosine Transform (DCT)-based feature dimensionality reduced approach for fingerprint matching is proposed. The DCT is applied on a small region around the core point of fingerprint image. The performance of our proposed method is evaluated on a small database of Bologna University and two large databases of FVC2000. A dimensionally reduced feature vector is formed using only approximately 19%, 7%, and 6% DCT coefficients for the three databases from Bologna University and FVC2000, respectively. We compared the results of our proposed method with the discrete wavelet transform (DWT) method, the rotated wavelet filters (RWFs) method, and a combination of DWT+RWF and DWT+(HL+LH) subbands of RWF. The proposed method reduces the false acceptance rate from approximately 18% to 4% on DB1 (Database of Bologna University), approximately 29% to 16% on DB2 (FVC2000), and approximately 26% to 17% on DB3 (FVC2000) over the DWT based feature extraction method.


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Cite this article
[APA Style]
Sangita Bharkad and Manesh Kokare (2017). Fingerprint Matching Based on Dimension Reduced DCT Feature Vectors. Journal of Information Processing Systems, 13(4), 852-862. DOI: 10.3745/JIPS.02.0017.

[IEEE Style]
S. Bharkad and M. Kokare, "Fingerprint Matching Based on Dimension Reduced DCT Feature Vectors," Journal of Information Processing Systems, vol. 13, no. 4, pp. 852-862, 2017. DOI: 10.3745/JIPS.02.0017.

[ACM Style]
Sangita Bharkad and Manesh Kokare. 2017. Fingerprint Matching Based on Dimension Reduced DCT Feature Vectors. Journal of Information Processing Systems, 13, 4, (2017), 852-862. DOI: 10.3745/JIPS.02.0017.