ÐDetecting faces in images with complex backgrounds is a difficult task. Our approach, which obtains state of the art results, is based on a new neural network model: the Constrained Generative Model (CGM). Generative, since the goal of the learning process is to evaluate the probability that the model has generated the input data, and constrained since some counterexamples are used to increase the quality of the estimation performed by the model. To detect side view faces and to decrease the number of false alarms, a conditional mixture of networks is used. To decrease the computational time cost, a fast search algorithm is proposed. The level of performance reached, in terms of detection accuracy and processing time, allows to apply this detector to a real world application: the indexation of images and videos.
Constant background hypothesis for background subtraction algorithms is often not applicable in real environments because of shadows, reflections, or small moving objects in the background: flickering screens in indoor scenes, or waving vegetation in outdoor ones. In both indoor and outdoor scenes, the use of color cues for background segmentation is limited by illumination variations when lights are switched or weather changes. This problem can be partially allievated using robust color coordinates or background update algorithms but an important part of the color information is lost by the former solution and the latter is often too specialized to cope with most of real environment constraints. This paper presents an approach using local kernel histograms and contour-based features. Local kernel histograms have the conventional histograms advantages avoiding their inherent drawbacks. Contour based features are more robust than color features regarding scene illumination variations. The proposed algorithm performances are emphasized in the experimental results using test scenes involving strong illumination variations and non static backgrounds.
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