Teacher-Student Adversarial Depth Hallucination to Improve Face Recognition
This work addresses the challenge of enhancing face recognition accuracy for security and biometric applications by integrating hallucinated depth data, representing an incremental improvement over existing methods.
The paper tackles the problem of generating depth images from single RGB images to improve face recognition systems, achieving performance boosts of +1.2%, +2.6%, and +2.6% on IIIT-D, EURECOM, and LFW datasets respectively.
We present the Teacher-Student Generative Adversarial Network (TS-GAN) to generate depth images from single RGB images in order to boost the performance of face recognition systems. For our method to generalize well across unseen datasets, we design two components in the architecture, a teacher and a student. The teacher, which itself consists of a generator and a discriminator, learns a latent mapping between input RGB and paired depth images in a supervised fashion. The student, which consists of two generators (one shared with the teacher) and a discriminator, learns from new RGB data with no available paired depth information, for improved generalization. The fully trained shared generator can then be used in runtime to hallucinate depth from RGB for downstream applications such as face recognition. We perform rigorous experiments to show the superiority of TS-GAN over other methods in generating synthetic depth images. Moreover, face recognition experiments demonstrate that our hallucinated depth along with the input RGB images boosts performance across various architectures when compared to a single RGB modality by average values of +1.2%, +2.6%, and +2.6% for IIIT-D, EURECOM, and LFW datasets respectively. We make our implementation public at: https://github.com/hardik-uppal/teacher-student-gan.git.