Abstract
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.
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Acknowledgment
The authors thank the Consorcio Ecuatoriano para el Desarrollo de Internet Avanzado -CEDIA-, and the Universidad de las Fuerzas Armadas -ESPE- for supporting the development of this work.
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Aulestia, P.S., Talahua, J.S., Andaluz, V.H., Benalcázar, M.E. (2017). Real-Time Face Detection Using Artificial Neural Networks. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_67
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