Authors : B. Vanathi, K. Shanmugam and V. Rymand Uthairaj
Abstract: This study focuses on an advanced mobile security system for M-commerce users to provide highly secured user friendly M-commerce transaction process. In previous researches propose security for M-commerce users invoking OTP. Also, there is analyzing system to identify the type of gender who uses the M-commerce application and forecast the demand in accordance. This study proposes finger print based biometric server extraction technique using Minutiae Map (MM) technique. In addition to that fingerprint from the user is sent in a secure way to the biometric server using Discrete Wavelet Transform (DWT). Every time a key is generated to authenticate the user from the service provider. Once the OTP is verified, user PIN is requested. The PIN is sent in a secure way using RC4 encryption algorithm. Also, gender classification is performed using Neural Network (NN). The fingerprint information is send to the service provider which improves the product quality and forecast the demand of the market. Gender classification done by ridge count, ridge thickness, White lines count and ridge count asymmetry and patterntype. Thus, our experimental results states NN provides improved accuracy in identifying gender classification.
B. Vanathi, K. Shanmugam and V. Rymand Uthairaj, 2016. A Secure M-Commerce Architecture for Service Provider to Improvize Quantity and Quality of the Products Using Fingerprint Authentication and Gender Classification. Asian Journal of Information Technology, 15: 232-242.