Small Noisy and Perspective Face Detection using Deformable Symmetric Gabor Wavelet Network
This addresses face detection in low-resolution, noisy images for applications like surveillance, but it appears incremental as it builds on existing wavelet-based methods with added symmetry constraints.
The paper tackled face detection in low-resolution images by proposing a deformable symmetric Gabor wavelet network model, which optimized parameters like rotation and perspective with symmetry constraints, showing promising results on a custom dataset and videos under challenging conditions.
Face detection and tracking in low resolution image is not a trivial task due to the limitation in the appearance features for face characterization. Moreover, facial expression gives additional distortion on this small and noisy face. In this paper, we propose deformable symmetric Gabor wavelet network face model for face detection in low resolution image. Our model optimizes the rotation, translation, dilation, perspective and partial deformation amount of the face model with symmetry constraints. Symmetry constraints help our model to be more robust to noise and distortion. Experimental results on our low resolution face image dataset and videos show promising face detection and tracking results under various challenging conditions.