Massively parallel palmprint identification system using GPU

• Published in: Cluster computing 2019-05, Vol.22 (Suppl 3), p.7201-7216
• Main Authors: Tariq, Syed Ali, Iqbal, Shahzaib, Ghafoor, Mubeen, Taj, Imtiaz A., Jafri, Noman M., Razzaq, Saad, Zia, Tehseen
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Biometrics; Computer Communication Networks; Computer Science; Field programmable gate arrays; Fourier transforms; Graphics processing units; Identification systems; Linear algebra; Operating Systems; Processor Architectures; Robustness (mathematics); Wavelet transforms
• ISSN: 1386-7857; 1573-7543
• DOI: 10.1007/s10586-017-1121-z

Automated human authentication is becoming increasingly important in today’s world due to increased need of security and surveillance applications deployed in almost all premises and installations. In this regard, palmprint biometric based identification has gained a lot of attention in recent years. However, due to large size of palmprint images and presence of principal lines, wrinkles, creases, and other noises, there are large number of inaccurate minutiae present. The computational requirement of palmprint identification is also quite large and it takes a lot of time to find identity of a palmprint in large database. In this study, a novel palmprint identification solution has been proposed that increases the accuracy of minutia detection based on improved frequency estimation and a novel region-quality based minutia extraction algorithm. Furthermore, a novel, efficient and highly accurate minutiae based encoding and matching algorithm is proposed that is designed to achieve maximum parallelism, and it is further accelerated using graphical processing unit. The results of the proposed palmprint identification demonstrate high accuracy and much faster identification speeds in comparison with current state of the art. Therefore, it can be considered as a robust, efficient and practical solution for palmprint based identification systems.