Region-aware scattering convolution networks for facial beauty prediction
This paper proposes a scattering convolutional network with region-aware facial attributes to obtain a mid-level representation for facial beauty prediction (FBP). Different from the previous works that only focus on the discriminative representation for prediction, this paper also considers the invariant properties of the facial representation that reduces the variances caused by the image transformations, such as rotations and translation. The proposed region-aware scattering convolution network (RegionScatNet) is based on a deep convolution network of scattering transforms (ScatNet) integrated with facial texture and shape features. It consists of three components, including: 1) Region Extraction to obtain the significant facial perception region with implicit shape features using region-aware mask; 2) Attributes Decomposition to separate the extracted region into detail and structure facial layers by a guided filter; 3) Scattering Convolution that computes the roto-translation invariant representation of facial detail and structure for FBP by cascading three-layer wavelets filters and non-linear modulus pooling. The comparisons with related deep learning-based methods illustrate the effectiveness of RegionScatNet for FBP. The evaluations with various prediction model (like SVR and Gaussian process) and with different facial variances (like rotaion) indicate the robustness of the RegionScatNet-based features.