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

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.

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

Semantic Scholar · Computer Science · 2016

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.

Source of record · Semantic Scholar

References (10)

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