  The extensive utilization of biometric authentication systems have emanated
attackers / imposters to forge user identity based on morphed images. In this
attack, a synthetic image is produced and merged with genuine. Next, the
resultant image is user for authentication. Numerous deep neural convolutional
architectures have been proposed in literature for face Morphing Attack
Detection (MADs) to prevent such attacks and lessen the risks associated with
them. Although, deep learning models achieved optimal results in terms of
performance, it is difficult to understand and analyse these networks since
they are black box/opaque in nature. As a consequence, incorrect judgments may
be made. There is, however, a dearth of literature that explains
decision-making methods of black box deep learning models for biometric
Presentation Attack Detection (PADs) or MADs that can aid the biometric
community to have trust in deep learning-based biometric systems for
identification and authentication in various security applications such as
border control, criminal database establishment etc. In this work, we present a
novel visual explanation approach named Ensemble XAI integrating Saliency maps,
Class Activation Maps (CAM) and Gradient-CAM (Grad-CAM) to provide a more
comprehensive visual explanation for a deep learning prognostic model
(EfficientNet-B1) that we have employed to predict whether the input presented
to a biometric authentication system is morphed or genuine. The
experimentations have been performed on three publicly available datasets
namely Face Research Lab London Set, Wide Multi-Channel Presentation Attack
(WMCA), and Makeup Induced Face Spoofing (MIFS). The experimental evaluations
affirms that the resultant visual explanations highlight more fine-grained
details of image features/areas focused by EfficientNet-B1 to reach decisions
along with appropriate reasoning.
