Beyond Vanilla Convolution
Face perception is an essential and significant problem in pattern recognition, concretely including Face Recognition (FR), Facial Expression Recognition (FER), and Race Categorization (RC). Though handcrafted features perform well on face images, Deep Convolutional Neural Networks (DCNNs) have brought new vitality to this field recently. Vanilla DCNNs are powerful at learning high-level semantic features, but are weak in capturing low-level image characteristic changes in illumination, intensity, and texture regarded as key traits in facial processing and feature extraction, which is alternatively the strength of human-designed feature descriptors. To integrate the best of both worlds, we proposed novel Random Pixel Difference Convolution (RPDC) which is efficient alternatives to vanilla convolutional layers in standard CNNs and can promote to extract discriminative and diverse facial features. By means of searched RPDC of high efficiency, we build S-RaPiDiNet, and achieve promising and extensive experiment results in FR ( ≈0.5 % improvement), FER (over 1% growth), and RC (0.25%–3% increase) than baseline network in vanilla convolution, showing strong generalization of RPDC.