Face detection by aggregated Bayesian network classifiers
Pattern Recognition Letters - In memory of Professor E.S. Gelsema
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Object Detection Using the Statistics of Parts
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian discriminating features method for face detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We propose a method of multiple context fusion based robust face detection scheme. It takes advantage of multiple contexts by combining color, illumination (brightness and light direction), spectral composition(texture) for environment awareness. It allows the object detection scheme can react in a robust way against dynamically changing environment. Multiple context based face detection is attractive since it could accumulate face model by autonomous learning process for each environment context category. This approach can be easily used in searching for multiple scale faces by scaling up/down the input image with some factor. The proposed face detection using the multiple context fusion shows more stability under changing environments than other detection methods. We employ Fuzzy ART for the multiple context- awareness. The proposed face detection achieves the capacity of the high level attentive process by taking advantage of the context-awareness using the information from illumination, color, and texture. We achieve very encouraging experimental results, especially when operation environment varies dynamically.