| dc.description.abstract | This research presents a targeted person searching scheme in digital videos. It is assumed that a user is given an exemplar video containing a person to be searched and a video, from which the scenes related the targeted person will be extracted. First, the exemplar video will be processed to select multiple representative images of persons, which will be shown on a user interface for the user to select the images of a targeted person. After choosing the images which best characterize the targeted person, the scheme will apply the face assessment process to build the model of the targeted person. The model can be employed to search that person in other videos. We hope that, by the assistance of such a scheme, searching people in videos can be facilitated. Such applications as actor comparison in videos, retrieval of people, or digital evidence collection can be achieved.
The scheme mainly relies on the face tracking method to find consecutive pictures or frames that contain human faces. With the acquirement of multiple images, we can build a more stable model of the targeted person and further develop a reliable face assessment method to choose better images for recognition. The assessment process not only avoids the images with poor quality, but also reduces operating time and efforts. The face assessment method takes four factors into consideration, including out-of-plane rotation, sharpness, brightness, and resolution. By analyzing parameters and recognition outcomes, we can understand the effects of different settings and interface influence, and investigate the utility of all aspects for face matching in videos. Experimental results show the accuracy of the proposed scheme and the possible improvement in the future. | en_US |