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Human face images usually appear with wide range of visual scales. Theexisting face representations pursue the bandwidth of handling scale variationvia multi-scale scheme that assembles a finite series of predefined scales.Such multi-shot scheme brings inference burden, and the predefined scalesinevitably have gap from real data. Instead, learning scale parameters fromdata, and using them for one-shot feature inference, is a decent solution. Tothis end, we reform the conv layer by resorting to the scale-space theory, andachieve two-fold facilities: 1) the conv layer learns a set of scales from realdata distribution, each of which is fulfilled by a conv kernel; 2) the layerautomatically highlights the feature at the proper channel and locationcorresponding to the input pattern scale and its presence. Then, we accomplishthe hierarchical scale attention by stacking the reformed layers, building anovel style named SCale AttentioN Conv Neural Network (\textbf{SCAN-CNN}). Weapply SCAN-CNN to the face recognition task and push the frontier of SOTAperformance. The accuracy gain is more evident when the face images are blurry.Meanwhile, as a single-shot scheme, the inference is more efficient thanmulti-shot fusion. A set of tools are made to ensure the fast training ofSCAN-CNN and zero increase of inference cost compared with the plain CNN.

Scale Attention for Learning Deep Face Representation: A Study Against Visual Scale Variation