Face quality assessment is important to improve the performance of face recognition system. For instance, it is required to
select images of good quality to improve recognition rate for the person of interest. Current methods mostly depend on
traditional image assessment, which use prior knowledge of human vision system. As a result, the quality score of face
images shows consistency with human vision perception but deviates from the processing procedure of a real face
recognition system. It is the fact that the state-of-art face recognition systems are all built on deep neural networks.
Naturally, it is expected to propose an efficient quality scoring method of face images, which should show high consistency
with the recognition rate of face images from current face recognition systems. This paper proposes a non-reference face
image assessment algorithm based on the deep features, which is capable of predicting the recognition rate of face images.
The proposed face image assessment algorithm provides a promising tool to filter out the good input images for the real
face recognition system to achieve high recognition rate.
Pedestrian tracking is an important and meaningful part of the computer vision topic. Given the position of pedestrian in
the first frame, our goal is to automatically determine the accurate position of the target pedestrian in every frame that
follows. Current tracking methods show good performance in short-term tracking. However, there are still some open
problems in real scenes, e.g. pedestrian re-identification under multi-camera surveillance and pedestrian tracking under
occlusions. In our paper, we proposed an efficient method for consecutive tracking, which can deal with the challenging
view changes and occlusions. Proposed tracker consists of short-time tracking mechanism and consecutive tracking
mechanism. The consecutive tracking mechanism will be activated while the target pedestrian is under occlusion or
changes dramatically in appearance. In consecutive tracking mechanism, proposed algorithm will detect the target
pedestrian using a coarse but fast feature as first level classifier and a fine feature as the last level classifier. After regaining
the accurate position of target pedestrian, the appearance model of the target pedestrian will be updated as historical
information and the short-time tracking mechanism will be activated again to continue tracking the target pedestrian.
Experimental results show that the proposed method can handle hard cases and achieve higher success rate than the current