Improved face recognition on video surveillance images using pose correction
Nur A'in Jamil, Usman Ullah Sheikh, Musa Mohd Mokji, Zaid Omar, Ab Al-Hadi Ab Rahman · 2017
Face recognition in video surveillance is challenging as there is no control on the quality of the face image captured especially under uncooperative situations with head pose variation, occlusion, and low-quality image. These issues create large discrepancy between probe and gallery images which affects the performance of recognition. To overcome these issues, pose correction is proposed to narrow the difference between probe and gallery images and image enhancer to highlight face image details. Face representation is improved by creating an average face image to reduce the effect of non-facial details captured such as background, occlusion and noise. The proposed methods are tested on ChokePoint dataset and resulted in improved face recognition.