| Summary: | Hyperspectral face recognition is a small sample size problem, where usually less than four hyperspectral cubes are available as training data. At the same time, hyperspectral face image acquires grayscale images over a series of continuous spectra which usually contain large redundant information or noise, especially in the near infrared spectrum bands. Therefore, dimensionality reduction and feature extraction are important tasks on this problem. This paper proposes a hierarchical clustering-based spectrum band selection method, which mitigates the influence of noise and extracts features from each spectra band by using the Gabor filter and the histograms of oriented gradients algorithm, In addition, the fusion of Hog and Gabor features was embedded into the nearest neighborhood-based classifier for performance comparison. The experimental results show that the proposed algorithm is time effective and provides robust performance.
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