  In this paper, we explore the correlation between different visual biometric
modalities. For this purpose, we present an end-to-end deep neural network
model that learns a mapping between the biometric modalities. Namely, our goal
is to generate a frontal face image of a subject given his/her ear image as the
input. We formulated the problem as a paired image-to-image translation task
and collected datasets of ear and face image pairs from the Multi-PIE and FERET
datasets to train our GAN-based models. We employed feature reconstruction and
style reconstruction losses in addition to adversarial and pixel losses. We
evaluated the proposed method both in terms of reconstruction quality and in
terms of person identification accuracy. To assess the generalization
capability of the learned mapping models, we also run cross-dataset
experiments. That is, we trained the model on the FERET dataset and tested it
on the Multi-PIE dataset and vice versa. We have achieved very promising
results, especially on the FERET dataset, generating visually appealing face
images from ear image inputs. Moreover, we attained a very high cross-modality
person identification performance, for example, reaching 90.9% Rank-10
identification accuracy on the FERET dataset.
