Representation Learning by Rotating Your Faces.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Large pose discrepancies in face images pose a significant challenge for automatic face recognition systems.
• Current methods often tackle pose invariance by either frontalizing faces or learning separate pose-invariant representations.

Purpose of the Study:

• To propose a novel approach that jointly learns face representations and performs face frontalization for enhanced pose-invariant recognition.
• To develop a method that leverages the synergy between representation learning and pose normalization.

Main Methods:

• Introduced a Disentangled Representation learning-Generative Adversarial Network (DR-GAN) with an encoder-decoder generator structure.
• Employed explicit disentanglement of identity representation from pose variations using a pose code and discriminator-based pose estimation.
• Designed DR-GAN to accept single or multiple input images for unified identity representation generation and synthetic image creation.

Main Results:

• The DR-GAN model demonstrated a capability for both generative (face synthesis) and discriminative (recognition) tasks.
• Achieved superior performance in learning robust identity representations and synthesizing realistic face images with arbitrary poses.
• Outperformed state-of-the-art methods in quantitative and qualitative evaluations on diverse face databases.

Conclusions:

• Jointly learning representations and frontalizing faces is more effective than sequential approaches.
• DR-GAN offers a powerful framework for pose-invariant face recognition and face image manipulation.
• The proposed method advances the state of the art in handling large pose variations in face recognition.