Integration of discriminative features and similarity-preserving encoding for finger vein image retrieval

Although some image retrieval methods were proposed to accelerate finger vein recognition, the insufficient feature (e.g., the number of vein point) and unfavorable encoding (e.g., predefined threshold based binarization) limited retrieval performance largely. In view of this problem, we develop a new retrieval framework, based on the integration of discriminative texture features and similarity-preserving binary codes. In detail, the vector and scalar features, measuring the gray level, gray difference, and gray gathering of image patch, are both used to represent finger vein image. And to improve the retrieval efficiency, the high-dimensional decimal features are further encoded into the compact binary patterns by principal component analysis (PCA) and similarity-preserving iterative quantization (ITQ). Experimental results on one large finger vein database prove that the proposed method can powerfully improve the retrieval accuracy and efficiency.