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A Fast and Accurate Face Detector Based on Neural Networks Raphae ¨ lFe ´raud, Olivier J. Bernier, Jean-Emmanuel Viallet, and Michel Collobert Abstract —Detecting 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. Index Terms —Combination of models, face detection, generative models, machine learning, neural networks, projection. æ 1I NTRODUCTION T O detect a face in an image means to find its position in the image plane (x,y) and its size or scale (z). Two broad classes of algorithms can perform this task. An image of a face can be considered as a set of features such as eyes, mouth, nose with constrained positions and size within an oval: an explicit model can be used. One of the simplest and fastest methods to realize the feature extraction is the projection of the image or the edge image on the vertical axis to find the eyes or the mouth and on the horizontal axis to locate the nose [22], [7], [19]. Several other methods are currently used to perform the feature extrac- tion: Gabor filter [25], oval detection [31], [24], etc. A similarity measurement between features is then used for face recognition or face detection task: Mahalanobis dis- tance [7], crosscorrelation [2], [7], [5], graph matching [25], elastic matching of features [40], decision tree [19], neural network [7], belief network [8]. .. Considering that an image of face is a particular event in the set all the possible images, extracted windows of the image can be analyzed to determine if these windows contain faces or parts of background. A probabilistic or statistic model can be used to analyze the pixels intensity of each subwindow (extracted window of the image). This model can be built with different methods: neural networks [6], [38], [12], [33], [20], [36], [29], [30], [13], [14], principal components analysis [35], [11], [15], [17], [18], [26], Kullback distance and maximum-likelihood method [10], Support Vector Machines [27], [28], etc. For face detection, the advantage of explicit models is usually the speed of the features extraction algorithm and the similarity measurement task in comparison to the methods directly based on the analysis of pixels intensity.
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This note was uploaded on 10/23/2010 for the course COMMINUCAT 123 taught by Professor Ali during the Spring '10 term at Masaryk University.

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