Improved Face Frontalization Using Multiple Side-Face Images

The task of generating frontal face images is a crucial challenge in the field of facial recognition. To address this, we propose a method that improves performance by leveraging multiple side-face images as input. By using multiple views of the face, our approach enhances the ability to accurately reconstruct the frontal face, even in cases with large pose variations, such as 90-degree angles. The use of multiple images allows for the capture of details that are otherwise missed in single-image methods, leading to more precise and identity-preserving frontal images. Experimental results show that our method significantly outperforms existing approaches, especially in handling extreme pose angles, demonstrating its effectiveness in improving face frontalization performance.