Resumen:
In this paper, we propose a model for face detection that works in both real-time and unstructured environments. for feature extraction, we applied the HOG (Histrograms of Oriented Gradients) technique in a cononical window. For classification, we used a feed-forward neural network. We tested the performance of the proposed model at detecting faces in sequences of color images. For this task, we created a database containing color image patches of faces and background to train the neural network and color images of 320 x 240 to test the model. The database is available at http://electronica-el.espe.edu.ec/actividad-estudiantil/face-database/. To achieve real-time, we split the model into several modules that run in parallel. the proposed model exhibited an accuracy of 91.4% and demonstrated robustness to changes in illumination, pose and occlusion. For the tests, we used a 2-core-2.5 GHz PC with 6 GB of RAM memory, where input frames of 320 x 240 were processed in an average time of 81 ms.
