Robust blurred face recognition using sample-wise kernel estimation and random compressed multi-scale local binary pattern histograms

Robust face recognition from blurred images is an important and challenging problem. We propose sample-wise kernel estimation (SWKE). For each sharp gallery sample, an optimal blur kernel making the distance between probe and corresponding blurred sample minimum is estimated. Then, the kernel is applied to blur the sharp gallery sample. All such blurred samples compose the adaptive blurred sample set acting as references during recognition. Next, we present random compressed multi-scale local binary pattern histograms (RCMSLBPH). Due to the sparseness of the histogram of each sub-region and each feature image, it is possible to accomplish large-margin dimensionality reduced without impairing great information loss by using random Gaussian projection matrix. Finally, we design two stage framework involving coarse recognition and fine recognition to obtain an optimized effectiveness-efficiency trade-off. The experimental results confirm the improvement of the proposed SWKE, RCMSLBPH and two stage framework on public face database recognition.