Template Extraction Algorithm using the Feature Analysis of Unique Patterns of Dog Nose Prints

Abstract

This paper proposes a dog nose-print template-extraction algorithm using a feature analysis technique for unique nose-print patterns to recognize dogs. Dog recognition using nose prints begins with an accurate extraction of the unique shape, position, and direction feature information comprising a nose print. The proposed algorithm consisted of determining the object center of gravity, object size, and the distance information between the object center of gravity and inner approximation vertices for each unique nose-print pattern. Based on the features extracted from one nose-print shape, nose-print matching was performed by estimating the correlation between the position and direction of the neighboring nose-print patterns. Nose-print recognition test results using the template generated by the proposed method showed 100% matching rates when the registered and authenticated nose prints were identical. For different nose prints, the matching error rate was 4.59%, 1.14%, 0.12%, 0.02%, and 0% for a similarity of 10, 20, 30, 40, and 50% or more, respectively. The experimental results showed that the matching success rates of the same nose print were excellent. No false authentication occurred in the matching results for different nose prints when the similarity exceeded 50%.