Feature level fusion of Face and Iris using Deep Features based on Convolutional Neural Networks
Supreetha Gowda H.D., Mohammad Imran, Hemantha Kumar G · 2018
In this work, we have proposed novel deep CNN framework architectures that effectively represent complex image characteristics which performs feature extraction in just two convolution layers and has successfully proved to be an reliable biometric verification system on employment of physiological traits face and iris for our system development. Extensive experiments in configuring the CNN hyper parameters such as number of convolution layers required, filters and its size in each layer, batch size, epochs, iterations and learning rate is a paramount, determining these factors truly depends on the nature of data and its size. Our work has relinquished our novel idea and has obtained 99% of GAR in unimodal biometric verification system itself and definitely the approach has rendered great results when compared with conventional feature extraction and classification techniques.