High-Resolution Face Recognition Via Deep Pore-Feature Matching

Shun-Cheung Lai, Minna Kong, Kin-Man Lam, Dong Li

Abstract: Because of the advancement of capturing devices, both image resolution and image quality have been significantly improved. Efficiently utilizing facial information is beneficial in enhancing the performance of face recognition methods. For high-resolution face images, pore-scale facial features can be observed. The positions and local patterns of pore features are biologically discriminative, so they can be explored for face identification. In this paper, we extend the previous work on pore-scale features, by proposing a new learning-based descriptor, namely PoreNet. Experiment results show that our proposed descriptor achieves an excellent performance on two high-resolution face datasets, namely Bosphorus and MultiPIE. More importantly, our proposed method significantly outperforms the state-of-the-art Convolutional Neural Network (CNN)-based face recognition method, when query faces are highly occluded. The code of our proposed method is available at: https://github.com/johnnysclai/PoreNet.