3D Model Based Pose Invariant Face Recognition from a Single Frontal View
This paper proposes a 3D model based pose invariant face recognition method that can recognize a face of a large rotation angle from its single nearly frontal view. The proposed method achieves the goal by using an analytic-to-holistic approach and a novel algorithm for estimation of ear points. Fir...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2007 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:24577 |
| Acceso en línea: | https://ddd.uab.cat/record/24577 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.134 |
| Access Level: | acceso abierto |
| Palabra clave: | Face Recognition Pose Estimation 3D face Model Single View Reconeixement de cara Model de cara 3D Vista única Reconocimiento de cara Modelo de cara 3D |
| Sumario: | This paper proposes a 3D model based pose invariant face recognition method that can recognize a face of a large rotation angle from its single nearly frontal view. The proposed method achieves the goal by using an analytic-to-holistic approach and a novel algorithm for estimation of ear points. Firstly, the proposed method achieves facial feature detection, in which an edge map based algorithm is developed to detect the ear points. Based on the detected facial feature points 3D face models are computed and used to achieve pose estimation. Then we reconstruct the facial feature points' locations and synthesize facial feature templates in frontal view using computed face models and estimated poses. Finally, the proposed method achieves face recognition by corresponding template matching and corresponding geometric feature matching. Experimental results show that the proposed face recognition method is robust for pose variations including both seesaw rotations and sidespin rotations. |
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