Robust degraded face recognition with novel face representation and multiscale fusion

Abstract

Recognizing degraded faces from low-resolution and blurry images is a common yet challenging task. We propose appealing solutions to this problem without any image reconstruction, without any limitation to blur type, and only using high-quality samples to design a classifier. Short-term Fourier transform (STFT) is an effective and concise transform for face recognition, yet its effectiveness depends on two important issues: one is face representation construction from STFT; the other is a scale-named window size of STFT. To deal with the first issue, we explore the increased discrimination brought by joint coding and using multiple frequency combinations. Specifically, we propose a novel local descriptor in which information of a pixel coming from two frequencies is jointly encoded and multiple two-frequency combinations are jointly utilized so as to construct a more descriptive and discriminative face representation. To deal with the second issue, we propose a multiscale fusion strategy that extracts multiple descriptors corresponding with multiple window sizes of STFT followed by equal weighted summation of outputs given by multiple scales. The experiments conducted on face databases confirm that state-of-the-art performance has been achieved by the proposed novel face representation and multiscale fusion strategy.