Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Face segmentation using skin-color map in videophone applications
IEEE Transactions on Circuits and Systems for Video Technology
Robust eye detection method for varying environment using illuminant context-awareness
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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We propose a cascade detection scheme by combining the color feature-based method and appearance-based method. In addition, the scheme employs illumination context-awareness so that the detection scheme can react in a robust way against dynamically changing illumination. Skin color provides rich information for extracting rough area of the face. Difficulties in detecting face skin color come from the variations in ambient light, image capturing devices, etc,. Appearance-based object detection, multiple Bayesian classifiers here, is attractive since it could accumulate object models by autonomous learning process. This approach can be easily adopted in searching for multiple scale faces by scaling up/down the input image with some factor. The appearance-based method shows more stability under changing illumination than other detection methods, but it is still bordered from the variations in illumination. We employ Fuzzy ART and RBFN for the illumination context- awareness. The proposed face detection achieves the capacity of the high level attentive process by taking advantage of the illumination context-awareness in both color feature-based detection and multiple Bayesian classifiers. We achieve very encouraging experimental results, especially when illumination condition varies dynamically.