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ACI Avances en Ciencias e Ingenierías

SECTION A: EXACT SCIENCES

Vol. 6 No. 2 (2014)

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

DOI
https://doi.org/10.18272/aci.v6i2.167
Submitted
September 30, 2015
Published
2014-12-19

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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