Abstract
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.
Highlights
Nowadays, biometrics [1,2,3] plays an increasingly important role in the modern information society and has drawn more and more research attention throughout the world
This is because the images captured at Red and NIR spectral bands contain some additional palm vein information, which plays an important role in classifying the images sharing similar palm lines
Each image captured at a single spectral band was decomposed into several bidimensional intrinsic mode functions (BIMFs) and a residue using fast and adaptive bidimensional empirical mode decomposition (FABEMD)
Summary
Biometrics [1,2,3] plays an increasingly important role in the modern information society and has drawn more and more research attention throughout the world. As an emerging and promising biometric characteristic, palmprint possesses some remarkable advantages such as high distinguishability, excellent user-friendliness and strong stability. Palmprint recognition [4,5,6,7] is to verify the identity of a person based on the palm information including principal lines, wrinkles and fine ridges. In contrast to password cards or identification cards, palmprint recognition is much more convenient, efficient and reliable with extensive and successful applications [8]. It is still faced with some challenges in real noisy environments, where the illumination condition may be unsatisfied or even.
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