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 (Histograms of Oriented Gradients) technique in a canonical 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 × 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 × 240 pixels were processed in an average time of 81 ms.
CITATION STYLE
Aulestia, P. S., Talahua, J. S., Andaluz, V. H., & Benalcázar, M. E. (2017). Real-time face detection using artificial neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10614 LNCS, pp. 590–599). Springer Verlag. https://doi.org/10.1007/978-3-319-68612-7_67
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