Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition

• URL: http://arxiv.org/abs/2010.12249v4
• Date: Sun, 3 Jul 2022 19:25:15 GMT
• Title: Multi Scale Identity-Preserving Image-to-Image Translation Network for Low-Resolution Face Recognition
• Authors: Vahid Reza Khazaie and Nicky Bayat and Yalda Mohsenzadeh
• Abstract summary: We propose an identity-preserving end-to-end image-to-image translation deep neural network. It is capable of super-resolving very low-resolution faces to their high-resolution counterparts while preserving identity-related information.
• Score: 7.6702700993064115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: State-of-the-art deep neural network models have reached near perfect face recognition accuracy rates on controlled high-resolution face images. However, their performance is drastically degraded when they are tested with very low-resolution face images. This is particularly critical in surveillance systems, where a low-resolution probe image is to be matched with high-resolution gallery images. super-resolution techniques aim at producing high-resolution face images from low-resolution counterparts. While they are capable of reconstructing images that are visually appealing, the identity-related information is not preserved. Here, we propose an identity-preserving end-to-end image-to-image translation deep neural network which is capable of super-resolving very low-resolution faces to their high-resolution counterparts while preserving identity-related information. We achieved this by training a very deep convolutional encoder-decoder network with a symmetric contracting path between corresponding layers. This network was trained with a combination of a reconstruction and an identity-preserving loss, on multi-scale low-resolution conditions. Extensive quantitative evaluations of our proposed model demonstrated that it outperforms competing super-resolution and low-resolution face recognition methods on natural and artificial low-resolution face data sets and even unseen identities.

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