Interactive sketch template creation from realistic pictures for tracing applications
Proceedings of the First International Conference on Internet Multimedia Computing and Service
Artistic paper-cut of human portraits
Proceedings of the international conference on Multimedia
Robust Gait Recognition by Learning and Exploiting Sub-gait Characteristics
International Journal of Computer Vision
A bi-objective optimization model for interactive face retrieval
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part II
Expression transfer for facial sketch animation
Signal Processing
A statistical-structural constraint model for cartoon face wrinkle representation and generation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
A face cartoon producer for digital content service
Mobile Multimedia Processing
Recognizing occluded faces by exploiting psychophysically inspired similarity maps
Pattern Recognition Letters
Object class detection: A survey
ACM Computing Surveys (CSUR)
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We present a hierarchical-compositional face model as a three-layer And-Or graph to account for the structural variabilities over multiple resolutions. In the And-Or graph, an And-node represents a decomposition of certain graphical structure expanding to a set of Or-nodes with associated relations; an Or-node functions as a switch variable pointing to alternative And-nodes. Faces are represented hierarchically: layer one treats each face as a whole; layer two refines the local facial parts jointly as a set of individual templates; layer three divides face into 15 zones and models facial features like eyecorners or wrinkles. Transitions between layers are realized by measuring the minimum-description-length given the face image complexity. Diverse face representations are formed by drawing from hierarchical dictionaries of faces, parts and skin features. A sketch captures the most informative part of a face in a concise and potentially robust representation. However, generating good facial sketches is challenging because of the rich facial details and large structural variations, especially in the high-resolution images. The representing power of our generative model is demostrated by reconstructing high-resolution face images and generating cartoon sketches. Our model is useful for applications such as face recognition, non-photorealisitc rendering, super-esolution, and low-bit rate face coding.