Masked SIFT with align-based refinement for contactless palmprint recognition

• Published in: IET biometrics 2019-03, Vol.8 (2), p.150-158
• Main Authors: ELSayed, Ahmed S, Ebeid, Hala M, Roushdy, Mohamed I, Fayed, Zaki T
• Format: Article
• Language: English
• Subjects: Accuracy; acquisition simplicity; align‐based refinement; Biometric recognition systems; Biometrics; biometrics (access control); Cameras; common contact‐based methods; comparison process; contactless environment; contactless hand databases; contactless palmprint recognition; Discriminant analysis; Error correction; false features; feature extraction; Fourier transforms; illumination variations; image matching; layout; less‐private nature; main modifications; masked SIFT; Methods; multidescriptors; multilines; Palm; palm lines; palm regions; palmprint recognition; query keypoints; Research Article; rotation difference; significant lines/wrinkles; traditional SIFT; transforms; user convenient; Verification; verification equal error rate; visibility
• ISSN: 2047-4938; 2047-4946; 2047-4946
• DOI: 10.1049/iet-bmt.2018.5012

Contactless palmprint is considered more user convenient than other biometrics due to its acquisition simplicity and less-private nature. Many challenges arise which affect the performance of common contact-based methods when applied to a contactless environment. For example, pose and illumination variations affect the layout and visibility of palm lines. This study proposes a SIFT-based method with three main modifications from the traditional SIFT. First, the palm regions with no significant lines/wrinkles are masked out to reduce the false features. A region with multi-lines is then described by multi-descriptors rather than a single one. Second, only query and target keypoints with small rotation difference are compared together, instead of comparing them all. This speed-up the comparison and enhance the accuracy, versus SIFT, by reducing the wrong matches. Third, an align-based refinement is applied to filter out the incorrect matches. The method is tested on three contactless hand databases; IITD, GPDS and Sfax-Miracl. It achieves a verification equal error rate of 0.72, 0.84 and 1.14% and a correct identification rate of 98.9, 99 and 98.9% on each database, respectively. These results are significantly better than the state-of-art methods on the same databases by 1.9% for verification and 3.2% for identification.