Artículo Científico. Real-time face detection using artificial neural networks.
- Format
- Article
- Status
- publishedVersion
- Description
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.
ESPE-L
- Publication Year
- 2017
- Language
- eng
- Topic
- DETECCIÓN DE ROSTROS EN TIEMPO REAL
HISTOGRAMAS DE GRADIENTES ORIENTADOS
REDES NUERONALES
- Repository
- Repositorio Universidad de las Fuerzas Armadas
- Get full text
- http://repositorio.espe.edu.ec/handle/21000/13928
- Rights
- openAccess
- License