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2015 | OriginalPaper | Buchkapitel

Hierarchical Convolutional Neural Network for Face Detection

verfasst von : Dong Wang, Jing Yang, Jiankang Deng, Qingshan Liu

Erschienen in: Image and Graphics

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a new approach of hierarchical convolutional neural network (CNN) for face detection. The first layer of our architecture is a binary classifier built on a deep convolutional neural network with spatial pyramid pooling (SPP). Spatial pyramid pooling reduces the computational complexity and remove the fixed-size constraint of the network. We only need to compute the feature maps from the entire image once and generate a fixed-length representation regardless of the image size and scale. To improve the localization effectiveness, in the second layer, we design a bounding box regression network to refine the relative high scored non-face output from the first layer. The proposed approach is evaluated on the AFW dataset, FDDB dataset and Pascal Faces, and it reaches the state-of-the-art performance. Also, we apply our bounding box regression network to refine the other detectors and find that it has effective generalization.

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Vorheriges Kapitel Hard Exudates Detection Method Based on Background-Estimation

Nächstes Kapitel Human-Object Interaction Recognition by Modeling Context

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Titel: Hierarchical Convolutional Neural Network for Face Detection
verfasst von: Dong Wang
Jing Yang
Jiankang Deng
Qingshan Liu
Verlag: Springer International Publishing
Buch: Image and Graphics
Print ISBN: 978-3-319-21962-2

Electronic ISBN: 978-3-319-21963-9

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-21963-9_34

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