Open Access Open Access  Restricted Access Subscription or Fee Access

An Efficient Support Vector Machine with Radial Basis Function for Passive Continuous Authentication System

K. Juliana Gnana Selvi, Shyamala Susan


Advancement in communication technology and computer applications in our day-to-day life highlights the strong need for user-friendly systems to secure our assets and protect our privacy without losing our identity. Generally, use a password to access a computer, Personal Identification Number (PIN) to access ATM, cryptographic techniques to access and view files and dozen others to access the internet and so on. These conventional methods of identification can be easily guessed, observed or forgotten. Hence the alternative option like biometric-based recognition for reliable and robust human identification has given more importance in the wide range of commercial sectors. In existing system, face   recognition is performed through hybrid swarm intelligence approach which uses both PSO and ABC. Hybrid ABC optimization algorithm was used to train a weighting mask for assisting the face recognition process. In this system training process can be finished offline, because of this it achieves less computation time.  However it does not produced satisfactory recognition accuracy. In order to improve the recognition accuracy the proposed system introduced a Radial Basis Function-Support Vector Machine (RBF-SVM) based biometric authentication system. In this proposed system preprocessing is performed by using median filter. Then the skin regions are detected from preprocessed images. And face detection is performed by using boosted classifiers with Haar-like features.  The same boosted classifiers are also used for eye detection. In this eye detection the left eye and right eye features extracted   to locate multifaces in the result. To ensure the high security eye pupil is detected by using circle Hough transforms.  The extracted features are given to RBF-SVM. It classifies the input images as matched or non-matched. The experimental results show that the proposed system achieves better performance compared with existing system in terms of accuracy, precision and recall.


Human Identification, Boosted Classifiers, RBF, SVM and Authentication

Full Text:



Niinuma, K., Park, U., & Jain, A. K. (2010). Soft biometric traits for continuous user authentication. IEEE Transactions on information forensics and security, 5(4), 771-780.

T. Rajyalakshmi, K.Koteswararao, K.Anuradha “The Design and Implementation of ID Authentication System Based on Fingerprint and Iris Identification”, International Journal of Professional Engineering Studies, Volume I-Issue 2, 2013.

S. Zhang, R. Janakiraman, T. Sim and S. Kumar, “Continuous Verification Using Multimodal Biometrics,” Proc. Second Int’l Conf. Biometrics, pp. 562-570, 2006.

Terence Sim, Sheng Zhang, Rajkumar Janakiraman and Sandeep Kumar, “Continuous Verification Using Multimodal Biometrics,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, 2007.

Antonia Azzini, Stefania Marrara, Roberto Sassi and Fabio Scotti, “A fuzzy approach to multimodal biometric continuous authentication, ”Fuzzy Optimal Decision Making, vol. 7, pp. 243-256, 2008.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.