17 research outputs found

    Multispectral image fusion for illumination-invariant palmprint recognition

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    <div><p>Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.</p></div

    Structure of the multispectral palmprint imaging device.

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    <p>Structure of the multispectral palmprint imaging device.</p

    Performance of tensor-based extreme learning machine with different number of hidden nodes.

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    <p>Performance of tensor-based extreme learning machine with different number of hidden nodes.</p

    Demonstration of how to generate a noised palmprint image.

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    <p>Demonstration of how to generate a noised palmprint image.</p

    Time cost of the proposed method.

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    <p>Time cost of the proposed method.</p

    Decompositions of a palmprint image using FABEMD: (a) the source image, (b) the 1st BIMF, (c) the 2nd BIMF, (d) the 3rd BIMF, (e) the 4th BIMF, and (f) the residue.

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    <p>Decompositions of a palmprint image using FABEMD: (a) the source image, (b) the 1st BIMF, (c) the 2nd BIMF, (d) the 3rd BIMF, (e) the 4th BIMF, and (f) the residue.</p

    Performance comparison with different fusion rules.

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    <p>Performance comparison with different fusion rules.</p

    Computational time of different classifiers.

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    <p>Computational time of different classifiers.</p

    Performance comparison with different multispectral palmprint recognition methods.

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    <p>Performance comparison with different multispectral palmprint recognition methods.</p
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