
Generate a face image of a subject given an ear image as the input [pdf] - pixxel
https://arxiv.org/abs/2006.01943
======
pixxel
>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.

...

If you don't have time to read the paper, do take a quick look at the image
results.

EDIT: I see HN strips .pdf from URL (good). Here's the direct PDF link
[https://arxiv.org/pdf/2006.01943.pdf](https://arxiv.org/pdf/2006.01943.pdf)

