The paper proposes and demonstrates a Deep Convolutional Neural Network (DCNN) architecture to identify users with disguised face attempting a fraudulent ATM transaction. The recent introduction of Disguised Face Identification (DFI) framework 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 deep convolutional neural network 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 in the images enables the network to demonstrate high accuracy in feature extraction and classification for all the different disguises. The widespread acceptance of Automated Teller Machine (ATM) and their omnipresence in the banking sector has engendered numerous security concerns. One of the most imperative concerns being, verifying the authenticity of the user. Evidently, most of the ATMs across the globe simply rely on a card and a Personal Identification Number (PIN) for authentication. However, in either case, it is plausible that the user is not authorised to transact. For instance, illegal practices like phishing, shoulder surfing, card fraud, stolen card can cause substantial monetary loss to the owner.To overcome and identify such practices, ATMs have an inbuilt camera which records 24x7. The current state of art ATM security works in the following way: After a fraudulent transaction, the owner of the corresponding bank account reports about the fraud. The police then investigates and goes through the footage recorded by the ATM camera to find the face of the imposter. Once the face is identified, the police searches for the imposter. Clearly, this security measure can be easily gamed by using artifacts or alterations like wigs, caps, eyeglasses, beard to cover the face for intentional disguises. As a result, BID6 stated that such face alterations can substantially degrade the performance of the system. Hence, this approach has a very low success rate which is unacceptable in banking sectors. Additionally, BID7 explained different openings and vulnerabilities that exist at the time of transactions due to fake entries and fake cards.Apparently, this chaos can be prevented by ensuring that the transaction proceeds only if the face is undisguised and reveal identity of the user. The proposed system extracts the user's face from the footage and checks if the face is disguised. The system is trained cleverly to identify such faces by an extensive pool of disguised and undisguised faces. If the face is disguised, the system will not allow the transaction to be proceeded, thereby preventing the imposter from stealing. To achieve this, the proposed system uses Deep Convolutional Neural Networks for image classification using statistical dimensionality reduction method. Deep networks have proved to be exceptional in computer vision problems BID9 BID4 . BID9 stated a three-layer cascading style which superficially captures the high level features and refines them to detect deeper features. Analogously, the proposed system uses a five-layer architecture, first 3 layers comprises of a convolutional layers followed by a pooling layers to learn the features of the following types of images : Disguised, Partially disguised and Undisguised.2 PROPOSED SYSTEM 2.1 EXISTING MECHANISMS Plenty of research work has been published in response to the ATM security problems and a lot of it relates to using machine learning to authenticate users. BID3 proposed a facebased authentication as identity test for users and the system uses facial recognition with biometric features. T. BID10 stated the applicability of image processing by amalgamation of Face Recognition System (FRS) in the identity verification process engaged in ATMs. BID2 proposed a framework to classify local facial regions of both visible and thermal face images into biometric (regions without disguise) and non-biometric (regions with disguise) classes and used the biometric patches for facial feature extraction and matching.
Proposed System can prevent impersonators with facial disguises from completing a fraudulent transaction using a pre-trained DCNN.
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