Fingerprint Matching Based on Dimension Reduced DCT Feature Vectors
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초록
In this work a Discrete Cosine Transform (DCT)-based feature dimensionality reduced approach forfingerprint matching is proposed. The DCT is applied on a small region around the core point of fingerprintimage. The performance of our proposed method is evaluated on a small database of Bologna University andtwo large databases of FVC2000. A dimensionally reduced feature vector is formed using only approximately19%, 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% onDB1 (Database of Bologna University), approximately 29% to 16% on DB2 (FVC2000), and approximately26% to 17% on DB3 (FVC2000) over the DWT based feature extraction method.
Abstract
In this work a Discrete Cosine Transform (DCT)-based feature dimensionality reduced approach forfingerprint matching is proposed. The DCT is applied on a small region around the core point of fingerprintimage. The performance of our proposed method is evaluated on a small database of Bologna University andtwo large databases of FVC2000. A dimensionally reduced feature vector is formed using only approximately19%, 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% onDB1 (Database of Bologna University), approximately 29% to 16% on DB2 (FVC2000), and approximately26% to 17% on DB3 (FVC2000) over the DWT based feature extraction method.
참고문헌 (16)
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