RP-Net: A Robust Polar Transformation Network for rotation-invariant face detection

Face detection is challenging in unconstrained environments, where it encounters various challenges such as orientation, pose, and occlusion. Deep convolutional neural networks , particularly cascaded ones, have greatly improved detection performance but still struggle with rotating objects due to limitations in the Cartesian coordinate system . Although data augmentation can mitigate this issue, it also increases computational demands. This paper introduces the Robust Polar Transformation Network (RP-Net) for rotation-invariant face detection. RP-Net converts the complex rotational problem into a simpler translational one to enhance feature extraction and computational efficiency. Additionally, the Advanced Spatial-Channel Restoration (ASCR) module optimizes facial landmark detection within polar domains and restores critical details lost during transformation. Experimental results on benchmark datasets show that RP-Net significantly improves rotation invariance over traditional CNNs and surpasses several state-of-the-art rotation-invariant face detection methods. • Polar transformations enable rotation-invariant face detection. • Enhancing facial landmark localization can improve detection robustness. • The polar module reduces the need for extensive data preprocessing before training. • The proposed method handles full 360-degree rotation and joint face detection and alignment.