Robust Degraded Face Recognition Using Enhanced Local Frequency Descriptor and Multi-scale Competition
This work addresses a common challenge in face recognition for applications like surveillance, but it is incremental as it builds on existing Local Frequency Descriptor methods.
The paper tackled the problem of recognizing degraded faces from low-resolution and blurred images by proposing an Enhanced Local Frequency Descriptor that jointly utilizes space and frequency information and a multi-scale competition strategy. Experiments on Yale and FERET databases demonstrated promising results.
Recognizing degraded faces from low resolution and blurred images are common yet challenging task. Local Frequency Descriptor (LFD) has been proved to be effective for this task yet it is extracted from a spatial neighborhood of a pixel of a frequency plane independently regardless of correlations between frequencies. In addition, it uses a fixed window size named single scale of short-term Frequency transform (STFT). To explore the frequency correlations and preserve low resolution and blur insensitive simultaneously, we propose Enhanced LFD in which information in space and frequency is jointly utilized so as to be more descriptive and discriminative than LFD. The multi-scale competition strategy that extracts multiple descriptors corresponding to multiple window sizes of STFT and take one corresponding to maximum confidence as the final recognition result. The experiments conducted on Yale and FERET databases demonstrate that promising results have been achieved by the proposed Enhanced LFD and multi-scale competition strategy.