IEEE Trans Pattern Anal Mach Intell - Multi-View Face Detection and Registration Requiring Minimal Manual Intervention.

Tópicos

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Resumo

Most face recognition systems require faces to be detected and localized a priori. In this paper, an approach to simultaneously detect and localize multiple faces having arbitrary views and different scales is proposed. The main contribution of this paper is the introduction of a face constellation, which enables multi-view face detection and localization. In contrast to other multi-view approaches that require many manually labeled images for training, the proposed face constellation requires only a single reference image of a face containing two manually indicated reference points for initialization. Subsequent training face images from arbitrary views are automatically added to the constellation (registered to the reference image) based on finding the correspondences between distinctive local features. Thus, the key advantage of the proposed scheme is the minimal manual intervention required to train the face constellation. We also propose an approach to identify distinctive correspondence points between pairs of face images in the presence of a large amount of false matches. To detect and localize multiple faces with arbitrary views, we then propose a probabilistic classifier based formulation to evaluate whether a local feature cluster corresponds to a face.

Resumo Limpo

face recognit system requir face detect local priori paper approach simultan detect local multipl face arbitrari view differ scale propos main contribut paper introduct face constel enabl multiview face detect local contrast multiview approach requir mani manual label imag train propos face constel requir singl refer imag face contain two manual indic refer point initi subsequ train face imag arbitrari view automat ad constel regist refer imag base find correspond distinct local featur thus key advantag propos scheme minim manual intervent requir train face constel also propos approach identifi distinct correspond point pair face imag presenc larg amount fals match detect local multipl face arbitrari view propos probabilist classifi base formul evalu whether local featur cluster correspond face

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