Multi model Personal Authentication using Finger vein and Face Images (MPAFFI)

Biometric based identifications are widely adopted for personnel identification. The unimodal recognition systems currently suffer from noisy data, spoofing attacks, biometric sensor data quality and many more. Robust personnel recognition considering multimodal biometric traits can be achieved. This paper introduces the Multimodal Personnel Authentication using Finger vein and Face Images (MPAFFI) considering the Finger Vein and Face 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 MPAFFI is compared with the existing recognition systems and the performance improvement is proved through the results obtained.