Face-Specific Data Augmentation for Unconstrained Face Recognition
| Autor: | Gerard Medioni, Tal Hassner, Gozde Sahin, Anh Tuấn Trần, Iacopo Masi |
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| Rok vydání: | 2019 |
| Předmět: |
Training set
Computer science business.industry face recognition deep learning face analysis Normalization (image processing) Pattern recognition 02 engineering and technology 3d shapes Facial recognition system Artificial Intelligence Gesture recognition 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Artificial intelligence business Software |
| Zdroj: | International Journal of Computer Vision. 127:642-667 |
| ISSN: | 1573-1405 0920-5691 |
| DOI: | 10.1007/s11263-019-01178-0 |
| Popis: | We identify two issues as key to developing effective face recognition systems: maximizing the appearance variations of training images and minimizing appearance variations in test images. The former is required to train the system for whatever appearance variations it will ultimately encounter and is often addressed by collecting massive training sets with millions of face images. The latter involves various forms of appearance normalization for removing distracting nuisance factors at test time and making test faces easier to compare. We describe novel, efficient face-specific data augmentation techniques and show them to be ideally suited for both purposes. By using knowledge of faces, their 3D shapes, and appearances, we show the following: (a) We can artificially enrich training data for face recognition with face-specific appearance variations. (b) This synthetic training data can be efficiently produced online, thereby reducing the massive storage requirements of large-scale training sets and simplifying training for many appearance variations. Finally, (c) The same, fast data augmentation techniques can be applied at test time to reduce appearance variations and improve face representations. Together, with additional technical novelties, we describe a highly effective face recognition pipeline which, at the time of submission, obtains state-of-the-art results across multiple benchmarks. Portions of this paper were previously published by Masi et al. (European conference on computer vision, Springer, pp 579–596, 2016b, International conference on automatic face and gesture recognition, 2017). |
| Databáze: | OpenAIRE |
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