Abstract: As a novel biometric modality, finger-knuckle-print (FKP) recognition has gained much attention for its
security and stability. Coding-based methods are considered as one of the most effective methods in this field. Such
methods can distinguish samples according to one single matching distance between two images computed from the
extracted features in the template matching stage. However, some fuzzy samples cannot be effectively distinguished
by one single matching distance, leading to false acceptance and false rejection. To address this problem, a
light-weight and effective method based on multi-dimensional matching distances fusion was proposed in this paper. The proposed method utilized the difference and complementarity between different matching distances of multiple
coding-based methods, and applied support vector machine (SVM) to the classification of the multi-dimensional
feature vectors constructed by the multiple matching distances. What’s more, the proposed method is a general
method, which can be easily embedded into the existing coding-based methods. Extensive experiments were
conducted for the range from two-dimensional matching distances to four-dimensional matching distances on the
public FKP database, PolyU-FKP. The results have shown that the proposed method can generally improve their
performances, with a maximum reduction of 22.19% in EER.

Key words: finger-knuckle-print recognition, multi-dimensional matching distances, difference complementarity; support vector machine, general method