In this paper, we propose a new and effective frontalization algorithm for frontal rendering of unconstrained face images, and experiment it for face recognition. Initially, a 3DMM is fit to the image, and an interpolating function maps each pixel inside the face region on the image to the 3D model's. Thus, we can render a frontal view without introducing artifacts in the final image thanks to the exact correspondence between each pixel and the 3D coordinate of the model. The 3D model is then back projected onto the frontalized image allowing us to localize image patches where to extract the feature descriptors, and thus enhancing the alignment between the same descriptor over different images. Our solution outperforms other frontalization techniques in terms of face verification. Results comparable to state-of-the-art on two challenging benchmark datasets are also reported, supporting our claim of effectiveness of the proposed face image representation.

Effective 3D based Frontalization for Unconstrained Face Recognition / Ferrari, Claudio; Lisanti, Giuseppe; Berretti, Stefano; Del Bimbo, Alberto. - STAMPA. - (2016), pp. 1047-1052. (Intervento presentato al convegno 23rd International Conference on Pattern Recognition (ICPR'16) tenutosi a Cancun, Mexico nel 4-8 Dcember, 2016) [10.1109/ICPR.2016.7899774].

Effective 3D based Frontalization for Unconstrained Face Recognition

FERRARI, CLAUDIO;LISANTI, GIUSEPPE;BERRETTI, STEFANO;DEL BIMBO, ALBERTO
2016

Abstract

In this paper, we propose a new and effective frontalization algorithm for frontal rendering of unconstrained face images, and experiment it for face recognition. Initially, a 3DMM is fit to the image, and an interpolating function maps each pixel inside the face region on the image to the 3D model's. Thus, we can render a frontal view without introducing artifacts in the final image thanks to the exact correspondence between each pixel and the 3D coordinate of the model. The 3D model is then back projected onto the frontalized image allowing us to localize image patches where to extract the feature descriptors, and thus enhancing the alignment between the same descriptor over different images. Our solution outperforms other frontalization techniques in terms of face verification. Results comparable to state-of-the-art on two challenging benchmark datasets are also reported, supporting our claim of effectiveness of the proposed face image representation.
2016
23rd International Conference on Pattern Recognition (ICPR'16)
23rd International Conference on Pattern Recognition (ICPR'16)
Cancun, Mexico
4-8 Dcember, 2016
Ferrari, Claudio; Lisanti, Giuseppe; Berretti, Stefano; Del Bimbo, Alberto
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1080439
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