Agrupamento de faces em vídeos digitais.

Abstract

Human faces are some of the most important entities frequently encountered in videos. As a result of the currently high volumes of digital videos production and consumption both personal and profissional videos, automatic extraction of relevant information from those videos has become an active research topic. Many efforts in this area have focused on the use of face clustering and recognition in order to aid with the process of annotating faces in videos. However, current face clustering algorithms are not robust to variations of appearance that a same face may suffer due to typical changes in acquisition scenarios. Hence, this thesis proposes a novel approach to the problem of face clustering in digital videos which achieves superior performance (in terms of clustering quality and computational cost) in comparison to the state-of-the-art, using reference video databases according to the literature. After performing a systematic literature review and experimental evaluations, the current approach has been proposed, which has the following modules: preprocessing, face detection, tracking, feature extraction, clustering, temporal similarity analysis, and spatial reclustering. The proposed approach for face clustering achieved the planned objectives obtaining better results (according to different metrics) than those presented by methods evaluated on the YouTube Celebrities videos dataset (KIM et al., 2008) and SAIVT-Bnews videos dataset (GHAEMMAGHAMI, DEAN e SRIDHARAN, 2013).