Abstract
Face recognition approaches often rely on equal image resolution for verification faces on two images. However, in practical applications, those image resolutions are usually not in the same range due to different image capture mechanisms or sources. In this work, we first analyze the impact of image resolutions on the face verification performance with a state-of-the-art face recognition model. For images, synthetically reduced to $5\, \times 5\, \mathrm{px}$ resolution, the verification performance drops from $99.23\%$ increasingly down to almost $55\%$. Especially, for cross-resolution image pairs (one high- and one low-resolution image), the verification accuracy decreases even further. We investigate this behavior more in-depth by looking at the feature distances for every 2-image test pair. To tackle this problem, we propose the following two methods: 1) Train a state-of-the-art face-recognition model straightforward with $50\%$ low-resolution images directly within each batch. \\ 2) Train a siamese-network structure and adding a cosine distance feature loss between high- and low-resolution features. Both methods show an improvement for cross-resolution scenarios and can increase the accuracy at very low resolution to approximately $70\%$. However, a disadvantage is that a specific model needs to be trained for every resolution-pair ...
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URL
https://arxiv.org/abs/2107.03769
