This paper proposes an efficient convolutional neural network architecture for apparent age estimation from single face image capable of top performance without the use of a pretraining stage. The architecture consists of 9 convolution layers and 2 max pool layers and requires only 79k parameters. We also improve the results by applying a weighted class distribution which assures that overwhelmingly represented classes do not skew the prediction results. These are compared to other results in literature for both methods that use pretraining and those that do not. The proposed method achieves estimation errors comparable to the human reference and to existing methods while being characterized by efficiency at both training and testing, it employs orders of magnitude fewer parameters and much less training time than other state-of-the-art methods.
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Efficient convolutional neural network for apparent age prediction
Semantic Scholar · Computer Science · 2019
Abstract
This paper proposes an efficient convolutional neural network architecture for apparent age estimation from single face image capable of top performance without the use of a pretraining stage. The architecture consists of 9 convolution layers and 2 max pool layers and requires only 79k parameters. We also improve the results by applying a weighted class distribution which assures that overwhelmingly represented classes do not skew the prediction results. These are compared to other results in literature for both methods that use pretraining and those that do not. The proposed method achieves estimation errors comparable to the human reference and to existing methods while being characterized by efficiency at both training and testing, it employs orders of magnitude fewer parameters and much less training time than other state-of-the-art methods.