Generative adversarial networks for 2D-based CNN pose-invariant face recognition

The computer vision community considers the pose-invariant face recognition (PIFR) as one of the most challenging applications. Many works were devoted to enhancing face recognition performance when facing profile samples. They mainly focused on 2D- and 3D-based frontalization techniques trying to synthesize frontal views from profile ones. In the same context, we propose in this paper a new 2D PIFR technique based on Generative Adversarial Network image translation. The used GAN is Pix2Pix paired architecture covering many generator and discriminator models that will be comprehensively evaluated on a new benchmark proposed in this paper referred to as Combined-PIFR database, which is composed of four datasets that provide profiles images and their corresponding frontal ones. The paired architecture we are using is based on computing the L1 distance between the generated image and the ground truth one (pairs). Therefore, both generator and discriminator architectures are paired ones. The Combined-PIFR database is partitioned respecting person-independent constraints to evaluate our proposed framework's frontalization and classification sub-systems fairly. Thanks to the GAN-based frontalization, the recorded results demonstrate an important improvement of 33.57% compared to the baseline.

Automated summary

This paper presents a new 2D PIFR technique based on Generative Adversarial Network image translation for improving face recognition performance when facing profile samples. By calculating the L1 distance between the generated image and the ground truth one, the proposed technique achieves a significant improvement of 33.57% compared to the baseline in the Combined-PIFR database evaluation.