Application of Deep Convolutional Neural Network to Prevent ATM Fraud by Facial Disguise Identification
Sumit Baburao Tamgale, Suraj Nandkishor Kothawade · 2017
The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent Automated Teller Machine (ATM) transaction. The recent introduction of Disguised Face Identification (DFI) framework [1] proves the applicability of deep neural networks for this very problem. All the ATMs nowadays incorporate a hidden camera in them and capture the footage of their users. However, it is impossible for the police to track down the impersonators with disguised faces from the ATM footage. The proposed DCNN model is trained to identify, in real time, whether the user in the captured image is trying to cloak his identity or not. The output of the DCNN is then reported to the ATM to take appropriate steps and prevent the swindler from completing the transaction. The network is trained using a dataset of images captured in similar situations as of an ATM. The comparatively low background clutter and less viewpoint variation in the images enable the network to demonstrate high accuracy in feature extraction and classification for all the different disguises.