Abstract

Biometric based identifications are widely used for personnel identification in the world. The unimodal recognition systems has many more disadvantages such as noisy data, spoofing attacks, biometric sensor data quality etc., Robust personnel recognition can be achieved using multimodal biometric traits. This paper introduces the Multimodal Personnel Authentication using Finger vein and Iris Images (MPAFII) considering the Finger Vein and Iris biometric traits. The use of Magnitude and Phase features obtained from Gabor Kernels is considered to define the biometric traits of personnel. The biometric feature space is reduced using Fischer Score and Linear Discriminate Analysis. Personnel recognition is achieved using the weighted K-nearest neighbor classifier. The experimental study presented in the paper considers the (Group of Machine Learning and Applications, Shandong University-Homologous Multimodal Traits) SDUMLA −HMT multimodal biometric dataset. The performance of the MPAFII is compared with the existing recognition systems and the performance improvement is proved through the results obtained in this work.