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2017 | OriginalPaper | Buchkapitel

Multi-branch CNN for Multi-scale Age Estimation

verfasst von : Marco Del Coco, Pierluigi Carcagnì, Marco Leo, Paolo Spagnolo, Pier Luigi Mazzeo, Cosimo Distante

Erschienen in: Image Analysis and Processing - ICIAP 2017

Verlag: Springer International Publishing

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Abstract

Convolutional Neural Networks (CNNs) attracted growing interest in recent years thanks to their high generalization capabilities that are highly recommended especially for applications working in the wild context. However CNNs rely on a huge number of parameters that must be set during training sessions based on very large datasets in order to avoid over-fitting issues. As a consequence the lack in training data is one of the greatest limits for the applicability of deep networks. Another problem is represented by the fixed scale of the filter in the first convolutional layer that limits the analysis performed through the subsequent layers of the network.

This paper proposes a way to overcome these problems by the use of a multi-branch convolutional neural network with a reduced deep. In particular its effectiveness for age group classification has been proved demonstrating how, on the one hand, the reduced deep avoids the overfitting issues, whereas, on the other hand, the multi-branch structure introduces a parallel multi-scale analysis capable to catch multiple size patterns.

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Titel: Multi-branch CNN for Multi-scale Age Estimation
verfasst von: Marco Del Coco
Pierluigi Carcagnì
Marco Leo
Paolo Spagnolo
Pier Luigi Mazzeo
Cosimo Distante
Verlag: Springer International Publishing
Buch: Image Analysis and Processing - ICIAP 2017
Print ISBN: 978-3-319-68547-2

Electronic ISBN: 978-3-319-68548-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68548-9_22

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