Fiducial Focus Augmentation for Facial Landmark Detection
Purbayan Kar Vishal Chudasama Naoyuki Onoe Pankaj Wasnik Vineeth Balasubramanian
Deep learning methods have led to significant improvements in the performance
on the facial landmark detection (FLD) task. However, detecting landmarks in
challenging settings, such as head pose changes, exaggerated expressions, or
uneven illumination, continue to remain a challenge due to high variability and
insufficient samples. This inadequacy can be attributed to the model's
inability to effectively acquire appropriate facial structure information from
the input images. To address this, we propose a novel image augmentation
technique specifically designed for the FLD task to enhance the model's
understanding of facial structures. To effectively utilize the newly proposed
augmentation technique, we employ a Siamese architecture-based training
mechanism with a Deep Canonical Correlation Analysis (DCCA)-based loss to
achieve collective learning of high-level feature representations from two
different views of the input images. Furthermore, we employ a Transformer +
CNN-based network with a custom hourglass module as the robust backbone for the
Siamese framework. Extensive experiments show that our approach outperforms
multiple state-of-the-art approaches across various benchmark datasets.