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Polish Information Processing Society
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Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

A compact deep convolutional neural network architecture for video based age and gender estimation

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DOI: http://dx.doi.org/10.15439/2016F472

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 787–790 ()

Full text

Abstract. In this paper research on a compact deep convolutional neural network (DCNN) architecture for age and gender estimation from facial images has been presented. The proposed solution was tested on the FERET and the Adience Benchmark databases. In the first case a 98.6\% accuracy for gender and 86.4\% for age estimation was obtained. For the Adience database, which contains images recorded in unconstrained conditions and is much more demanding, a 62.0\% for gender and 42.0\% for age accuracy was obtained. When compared to the reference results on a much larger network, the performance should be considered as satisfactory. The research shows that a compact DCNN with small input images can provide quite good classification results.

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