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 …
Face-Specific Data Augmentation for Unconstrained Face Recognition / Masi, I; Tran, A; Hassner, T; Sahin, G; Medioni, G. - In: INTERNATIONAL JOURNAL OF COMPUTER VISION. - ISSN 0920-5691. - (2019).
Scheda prodotto non validato
Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo
|Titolo:||Face-Specific Data Augmentation for Unconstrained Face Recognition|
|Data di pubblicazione:||2019|
|Citazione:||Face-Specific Data Augmentation for Unconstrained Face Recognition / Masi, I; Tran, A; Hassner, T; Sahin, G; Medioni, G. - In: INTERNATIONAL JOURNAL OF COMPUTER VISION. - ISSN 0920-5691. - (2019).|
|Appartiene alla tipologia:||01a Articolo in rivista|