Neural Photofit: Gaze-based Mental Image Reconstruction
Florian Strohm,
Ekta Sood,
Sven Mayer,
Philipp Müller,
Mihai Bâce,
Andreas Bulling
Proc. IEEE International Conference on Computer Vision (ICCV),
pp. 245-254,
2021.
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We propose a novel method that leverages human fixations to visually decode the image a person has in mind into a photofit (facial composite). Our method combines three neural networks: An encoder, a scoring network, and a decoder. The encoder extracts image features and predicts a neural activation map for each face looked at by a human observer. A neural scoring network compares the human and neural attention and predicts a relevance score for each extracted image feature. Finally, image features are aggregated into a single feature vector as a linear combination of all features weighted by relevance which a decoder decodes into the final photofit. We train the neural scoring network on a novel dataset containing gaze data of 19 participants looking at collages of synthetic faces. We show that our method significantly outperforms a mean baseline predictor and report on a human study that shows that we can decode photofits that are visually plausible and close to the observer’s mental image. Code and dataset available upon request.
Code: Available upon request.
Dataset: Available upon request.
@inproceedings{strohm21_iccv,
title = {Neural Photofit: Gaze-based Mental Image Reconstruction},
author = {Strohm, Florian and Sood, Ekta and Mayer, Sven and Müller, Philipp and Bâce, Mihai and Bulling, Andreas},
year = {2021},
booktitle = {Proc. IEEE International Conference on Computer Vision (ICCV)},
doi = {10.1109/ICCV48922.2021.00031},
pages = {245-254}
}
