  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.
