Transformation guided representation GAN for pose invariant face recognition

Face recognition is an important topic in the field of computer vision and has been a vital biometric technique for identity authentication. It is widely used in areas such as public security, military, and daily life. However, face recognition is inherently a challenging problem due to variations in poses, facial expressions, age, and occlusion. In this work, we propose a generative adversarial network (GAN) architecture that disentangles identity and pose variations to learn generative and discriminative representations for pose-invariant face recognition. We use an iterative warping scheme that achieves better results than with the use of a single generator. The features from the encoder are considered pose-invariant features for face recognition, and evaluations on databases demonstrate the usefulness of this approach over prior methods. For example, we report 97.0% (+ 12.7%) and 90.5% (+ 8.4%) accuracy on the Feret and Caspeal datasets compared to 78.2% achieved by the best method without warping. In particular, there are two notable novelties. First, the disentangled architecture GAN (D-GAN) performs frontal face synthesis via an encoder-decoder structure in the generator with the pose variations provided to the decoder and discriminator. Second, we utilize the generator encoder as a spatial transformer network that seeks realistic image synthesis in the geometric warp parameter space.

Automated summary

The study introduces a generative adversarial network architecture for pose-invariant face recognition, which achieves better results through an iterative warping scheme. Evaluations demonstrate the method's higher accuracy compared to prior methods.