Efficient Visual Recognition Using the Hausdorff Distance
Efficient Visual Recognition Using the Hausdorff Distance
Neural Network-Based Face Detection
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FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
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Robust Facial Feature Location on Gray Intensity Face
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Interactive Hausdorff distance computation for general polygonal models
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WSEAS Transactions on Information Science and Applications
Eye localization in low and standard definition content with application to face matching
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Eye pupil localization with an ensemble of randomized trees
Pattern Recognition
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Precise localization of eye centers with multiple cues
Multimedia Tools and Applications
Face Alignment by Explicit Shape Regression
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The localization of human faces in digital images is a fundamental step in the process of face recognition. This paper presents a shape comparison approach to achieve fast, accurate face detection that is robust to changes in illumination and background. The proposed method is edge-based and works on grayscale still images. The Hausdorff distance is used as a similarity measure between a general face model and possible instances of the object within the image. The paper describes an efficient implementation, making this approach suitable for real-time applications. A two-step process that allows both coarse detection and exact localization of faces is presented. Experiments were performed on a large test set base and rated with a new validation measurement.