Abstract
Biometrics identification technology has gradually become a research hotspot in the field of information processing. As a step of biometrics identification technology, feature extraction processing plays a vital role. Aiming at the shortcoming of existing feature extraction algorithms are vulnerable to noise interference, large amount of calculation, high dimension and incomplete features, this paper proposes an improved MB-LBP feature extraction algorithm based on reduced-dimensional HOG. The algorithm uses MB-LBP to extract texture features of the image, and uses reduced-dimensional HOG to extract edge features. Through serial fusion, complete image features are formed. The proposed algorithm is verified by experimental simulation comparison with HOG feature extraction, dimensionality reduction HOG feature extraction and MB-LBP feature extraction. The algorithm in this paper has the characteristics of strong anti-interference ability, low dimension and complete features in feature extraction.
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