Multispectral image fusion for illumination-invariant palmprint recognition

• Published in: PloS one Vol. 12; no. 5; p. e0178432
• Main Authors: Lu, Longbin, Zhang, Xinman, Xu, Xuebin, Shang, Dongpeng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 30.05.2017; Public Library of Science (PLoS)
• Subjects: Accuracy; Adaptive filters; Algorithms; Attention; Biometric Identification - methods; Biometrics; Biometry; Classification; Coding; Colleges & universities; Comparative studies; Compensation; Computation; Computer and Information Sciences; Computer programs; Computer vision; Cost analysis; Cybernetics; Data acquisition; Data collection; Data processing; Decomposition; Electronics; Embedding; Engineering; Engineering and Technology; Expert systems; Funds; Genetic algorithms; Grants; Humans; I.R. radiation; Identification; Illumination; Image classification; Image processing; Information processing; Instrumentation; Intelligence; International conferences; Internet; Learning; Learning algorithms; Light sources; Lighting; Machine learning; Matching; Methods; Neurocomputing; Pattern recognition; Personal information; Physical Sciences; Qualitative analysis; Redundancy; Remote sensing; Research and Analysis Methods; Security; Sensors; Signal processing; Spectra; Spectral bands; Stability; Statistical analysis; Visible spectrum; Vision; Wavelet transforms; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0178432

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.