  Biometric based authentication is currently playing an essential role over
conventional authentication system; however, the risk of presentation attacks
subsequently rising. Our research aims at identifying the areas where
presentation attack can be prevented even though adequate biometric image
samples of users are limited. Our work focusses on generating photorealistic
synthetic images from the real image sets by implementing Deep Convolution
Generative Adversarial Net (DCGAN). We have implemented the temporal and
spatial augmentation during the fake image generation. Our work detects the
presentation attacks on facial and iris images using our deep CNN, inspired by
VGGNet [1]. We applied the deep neural net techniques on three different
biometric image datasets, namely MICHE I [2], VISOB [3], and UBIPr [4]. The
datasets, used in this research, contain images that are captured both in
controlled and uncontrolled environment along with different resolutions and
sizes. We obtained the best test accuracy of 97% on UBI-Pr [4] Iris datasets.
For MICHE-I [2] and VISOB [3] datasets, we achieved the test accuracies of 95%
and 96% respectively.
