BioPay: A Secure Payment Gateway through Biometrics

Due to emerging technological developments, major enhancements are taking place in the area of a secure and quick transaction. BioPay being a secure payment method is a one-step ahead. In the proposed methodology, there is no involvement of any credit or debit card or any other account information like OTP or CVV; it solely depends upon some unique identifying characteristic of a human known as biometrics. This work proposes a novel method that allows users to complete transactions quickly and securely using face and finger recognition. The transaction initiates with scanning face features and matching it with the database which in turn retrieves all the information associated with that customer account. After that, the system will scan the fingerprints of the subject and verify the transaction. This methodology can be implemented in ATMs and smartphones resulting in enhanced security and flexibility for payment purposes.

Perbandingan Ekstraksi Fitur dan Proses Matching pada Autentikasi Sidik Jari Manusia

ABSTRAK Sidik jari merupakan biometrik yang sering digunakan dalam teknologi autentikasi. Terdapat banyak metode yang bisa digunakan untuk membuat sistem klasifikasi sidik jari. Maximum Curvature Points (MCP) umumnya digunakan untuk ekstraksi citra pembuluh darah jari yang juga digunakan sebagai autentikasi. Pada penelitian ini akan diuji performansi dari metode MCP jika dibandingkan dengan metode yang umum digunakan pada proses pengenalan sidik jari, yaitu Hit and Miss Transform (HMT). Perbedaan domain, yaitu spasial pada Normalized Cross Correlation (NCC) dan frekuensi pada Phase Correlation (PC) dalam proses matching ternyata juga mempengaruhi performansi sistem. Hasilnya menunjukkan bahwa penggunakaan metode MHTNCC memiliki tingkat akurasi yang lebih baik dalam pengenalan sidik jari yaitu 92% untuk pengenalan ibu jari dan 98% untuk pengenalan jari telunjuk, dibandingkan dengan menggunakan metode MCP-PC yang hanya memiliki tingkat akurasi sebesar 88% untuk pengenalan ibu jari dan 92% untuk pengenalan jari telunjuk. Kata kunci: sidik jari, MCP, HMT, phase correlation, normalized cross correlation ABSTRACT Fingerprint is one of the biometric systems that are often used in an authentication technology. There are many methods that can be used to develop fingerprint’s classification system. Maximum Curvature Points (MCP) are generally used for finger vein image extraction which is also used as authentication. MCP performance will be compared to common method in finger print recognition, Hit and Miss Transform (HMT). Using different domains, spatial in Normalized Cross Correlation (NCC) and frequency in Phase Correlation (PC) affect the system performance. The results show that the application of HMT-NCC more accurate in terms of finger print’s recognition, 92% in accuracy for thumb recognition and 98% accuracy for index finger recognition, while MCP-PC is only reach 88% in accuracy for thumb recognition and 92% accuracy for index finger recognition. Keywords: fingerprint, MCP, HMT, phase correlation, normalized cross correlation