Method for estimating potential recognition capacity of texture-based biometrics

• Published in: IET biometrics 2018-11, Vol.7 (6), p.581-588
• Main Authors: Wang, Yiding, Yang, Chenyan, Shark, Lik-Kwan
• Format: Article
• Language: English
• Subjects: bespoke hand vein system; Biometric identification; Biometric recognition systems; Biometrics; block transform coding; Classification; commercial fingerprint system; constituent biometric component; data compression; Estimation; feature extraction; figure‐of‐merit; fingerprint identification; Fingerprints; image block; Image coding; Image compression; image texture; image‐based biometric system; potential recognition capacity estimation; Research Article; statistical analysis; statistical randomness; statistical variability; Statistics; Texture recognition; texture‐based biometrics; transforms; vein recognition; Veins
• ISSN: 2047-4938; 2047-4946; 2047-4946
• DOI: 10.1049/iet-bmt.2017.0052

When adopting an image-based biometric system, an important factor for consideration is its potential recognition capacity, since it not only defines the potential number of individuals likely to be identifiable, but also serves as a useful figure-of-merit for performance. Based on block transform coding commonly used for image compression, this study presents a method to enable coarse estimation of potential recognition capacity for texture-based biometrics. Essentially, each image block is treated as a constituent biometric component, and image texture contained in each block is binary coded to represent the corresponding texture class. The statistical variability among the binary values assigned to corresponding blocks is then exploited for estimation of potential recognition capacity. In particular, methodologies are proposed to determine appropriate image partition based on separation between texture classes and informativeness of an image block based on statistical randomness. By applying the proposed method to a commercial fingerprint system and a bespoke hand vein system, the potential recognition capacity is estimated to around 1036 for a fingerprint area of 25 mm2 which is in good agreement with the estimates reported, and around 1015 for a hand vein area of 2268 mm2 which has not been reported before.