Finger Vein Code: From Indexing to Matching

Vein pattern-based methods powerfully boost the recognition accuracy of finger veins, but real-time recognition cannot be guaranteed, especially in large-scale applications. Moreover, previous studies focused on either the matching task to enhance the accuracy or the indexing task to improve the efficiency. This paper proposes a finger vein code indexing method and combines it with a finger vein pattern matching method into an integration framework for improving both accuracy and efficiency. With the extracted vein patterns, the direction of each vein segment is detected and represented by the elliptical direction map as a feature for indexing, which will be encoded into a binary code by the angle K-means. The similarity between vein direction codes is measured by the grouped hamming distance in indexing, and further weighted by the overlap degree of the corresponding vein patterns to return the candidates for the probe. In addition, based on the above distance measurement, only vein segments with the same direction code are considered in following probe-to-candidate matching. Experimental results indicate that our indexing method outperforms the state-of-the-art methods and has competitive potential in performing the matching task. The results also indicate that the integration framework highly improves the identification efficiency with a slight improvement on the accuracy.