Vol. 6 No. 2 (2014), SECTION A: EXACT SCIENCES
Vol. 6 No. 2 (2014)

Implementing a convolution neural network for recognizing poses in images of faces

SECTION A: EXACT SCIENCES
Published 2014-12-19
Paul Méndez
Universidad San Francisco de Quito
Julio Ibarra
Universidad San Francisco de Quito
Portada: Volumen 6 - Número 2
PDF (Spanish)

Keywords

Convolutional neuronal network
deep neuronal networks
deep learning
machine learning
face recognition
face pose detection

How to Cite

Méndez, P., & Ibarra, J. (2014). Implementing a convolution neural network for recognizing poses in images of faces. ACI Avances En Ciencias E Ingenierías, 6(2). https://doi.org/10.18272/aci.v6i2.167
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Abstract

Convolutional neural networks belong to a set of techniques grouped under deep learning, a branch of machine learning, which has proven successful in recent years in image and voice recording recognition tasks. This paper explores the use of deep convolutional neural networks in the recognition of horizontal poses outside the plane. We propose a convolutional neural network architecture based on OpenCV open source libraries for classification of images of human faces within seven default poses. We present in details the optimized design of our architecture and our learning strategy.

The classifier trained on a set of 2600 images of sizes: 33 × 33, 41 × 41, 65 × 65 y 81 × 81, achive an recognition rate of 85%, higher than the 78% achieved with the Eigenfaces algorithm, with nearly the same execution time.

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