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Guodong Guo
Researcher at Baidu
Publications - 264
Citations - 10614
Guodong Guo is an academic researcher from Baidu. The author has contributed to research in topics: Computer science & Facial recognition system. The author has an hindex of 43, co-authored 218 publications receiving 8493 citations. Previous affiliations of Guodong Guo include Microsoft & National University of Singapore.
Papers
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Journal ArticleDOI
Age Synthesis and Estimation via Faces: A Survey
TL;DR: The complete state-of-the-art techniques in the face image-based age synthesis and estimation topics are surveyed, including existing models, popular algorithms, system performances, technical difficulties, popular face aging databases, evaluation protocols, and promising future directions are provided.
Journal ArticleDOI
Image-Based Human Age Estimation by Manifold Learning and Locally Adjusted Robust Regression
TL;DR: The age manifold learning scheme for extracting face aging features is introduced and a locally adjusted robust regressor for learning and prediction of human ages is designed, which improves the age estimation accuracy significantly over all previous methods.
Proceedings ArticleDOI
Face recognition by support vector machines
TL;DR: The potential of SVM on the Cambridge ORL face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details, is illustrated.
Proceedings ArticleDOI
Human age estimation using bio-inspired features
TL;DR: This work investigates the biologically inspired features (BIF) for human age estimation from faces with significant improvements in age estimation accuracy over the state-of-the-art methods and proposes a new operator “STD” to encode the aging subtlety on faces.
Journal ArticleDOI
Content-based audio classification and retrieval by support vector machines
Guodong Guo,Stan Z. Li +1 more
TL;DR: The SVMs with a binary tree recognition strategy are used to tackle the audio classification problem and experimental comparisons for audio retrieval are presented to show the superiority of this novel metric, called distance-from-boundary (DFB).