Abstract
In this paper, we describe a model of a convolutional neural network for automatic simultaneous extraction of several features of the person’s appearance from an image. The proposed model has the form of a deep convolutional neural network with common initial layers and several probabilistic outputs. This neural network has the high accuracy of a convolutional network and can simultaneously extract a number of features of the appearance in the time that is required to extract one feature of the appearance. The neural network is tested using photographs from the LWF database. As features of the appearance of a person, we use the person’s sex, a mustache, and a beard. The accuracy of identifying each feature is no less than 91.5%, which is one of the best results for the LWF database. This model of a neural network can be used for simultaneous identification of a greater number of features of the person’s appearance without a significant increase in operating time.
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This paper uses the materials of the report submitted at the 11th International Conference “Pattern Recognition and Image Analysis: New Information Technologies,” Samara, Russia, September 23–28, 2013.
Artem I. Kukharenko was born in 1990. Graduated from Moscow State University, Department of Computational Mathematics and Cybernetics, in 2012. The author of three papers.
Anton S. Konushin was born in 1980. Graduated from Moscow State University, Department of Computational Mathematics and Cybernetics, in 2002. Received his Candidate’s degree in 2005. The author of 57 papers.
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Kukharenko, A.I., Konushin, A.S. Simultaneous classification of several features of a person’s appearance using a deep convolutional neural network. Pattern Recognit. Image Anal. 25, 461–465 (2015). https://doi.org/10.1134/S1054661815030128
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DOI: https://doi.org/10.1134/S1054661815030128