Non-conventional biometrics using DOST features for secure cloud deployment

Abstract

The current decade has been experiencing a lot of research opportunities and challenges in the domain of biometric security. Simultaneously, Internet-of-Things (IoT) is also gaining seed functionality in early aspects of human lives. Cloud computing is the central thematic node in these two areas. In this work, we have proposed a novel biometric authentication scheme which is not based on conventional minutiae features, rather it is based on the frequency domain information of the fingerprint image. Input fingerprint is subjected to suitable quick pre-processing and then the discrete orthonormal Fourier transformation (DOST) features are extracted. Through suitable feature selection, chosen feature points are given to the classification stage where the recognition is accomplished using the standard AdaBoost-RF (AdaBoost Random Forest) algorithm. An overall accuracy of 98.5% has been obtained on a $k$ -fold cross-validation ( $k=5$ ) measure. The result obtained is compared with that of the four other state-of-the-art methodologies. On this, the proposed method outperforms the others in terms of accuracy and time of computation.