A Comparative Analysis of Deep-Learning Models with Novel Hybrid Biometric Modality Deep-Learning Network (BIOMODEN) to cognize Classification Accuracy of Fused Biometric Image

Image processing through feature identification and classification have entailed various phases of algorithmic effectuation. However, the need to scrupulously diverge into the precise stratification of feature identification has always remained a challenge. Deep-learning models in the recent times, have bolstered the accuracy of classification through the various networks and layered-models that have been established. However, this paper strives to render a bimodal approach of biometric image processing, along with establishing an elaborate indagation on the juxtaposed analysis of biometric image classification through transfer learning from the pre-trained models such as Darknet-19 and GoogleNet. The research also delves into the implementation of classification accuracy through hybrid contrived network model called the Biometric Modality Deep-Learning Network (BIOMODEN) which further augments the emphasis to comprehend and effectively procure the zenith of classification accuracy for specifically incorporated biometric fused modality images. The implementation of this study is carried out in MATLAB, and the results are successfully obtained.