Reduced Optimal Feature Based Biometric Authentication Using MALO-MKSVM Techniques

摘要

Biometric authentication is referred to as a realistic authentication which traits used is distinct, and quantifiable to recognize one individual. Depending on the level of security required, unimodal based authentication mechanisms are prone to numerous security attacks. In this paper, we propose a multimodal based biometric recognition framework which will improve the security level by using more than one type of biometric scanner. A new multimodal feature extraction technique has been proposed to reduce the features by utilizing Probabilistic Principal Component Analysis (PPCA) model by the way of choosing optimal features with the assistance of Modified Ant Lion Optimization (MALO). Finally, the recognized and non-recognized images are accomplished by the formation of a new classification model i.e. Multi Kernel Support Vector Machine (MKSVM). From this procedure, the result showed that a high recognition rate and also the most extreme accuracy accomplished in this work.