3D Face Recognition Based on Hybrid Data

Abstract

Unlike 2D face recognition (FR), the problem of insufficient training data is a major difficulty in 3D face recognition. Traditional Convolutional neural networks (CNNs) can not comprehensively learn all proper filters for FR applications. We embed a handcrafted feature map into our CNN framework—A hybrid data representation is proposed for 3D face. Furthermore, we use a Squeeze-Excitation block to learn the weights of data channels from training face datasets. To overcome the bias of training model based on a small 3D dataset, transfer learning is applied by fine-turning pre-training models, which is trained based on a large 2D face datasets. Tests show that, under challenge conditions such as expression and occlusion, our method outperforms other state-of-the-art methods and can run in real-time.