Head pose estimation on deep CNN models

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Authors
Shao, Wenxin
Issue Date
2017-08
Type
Electronic thesis
Thesis
Language
ENG
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Electrical engineering
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Abstract
Head pose estimation is an important computer vision task. Its applications can benefit people's daily life. In this thesis, our goal is to solve the head pose estimation problem using deep convolutional neural networks. The first task is to perform head pose estimation as a classification task. We propose a multimodal convolutional neural network(CNN) for head pose classification. The architecture of the model consists of three pathways whose inputs are face image, head image, and facial landmarks, which respectively capture the face appearance, facial context, and facial shape. We first perform the experiments on benchmark datasets. Then we perform head pose classification on low-quality driving videos. In order to deal with the noises in the videos, we propose the Max-Feature Map(MFM) with the help of Network-In-Network(NIN) for the CNN model, which has a better capability of handling the noises and the feature selection.
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August 2017
School of Engineering
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Rensselaer Polytechnic Institute, Troy, NY
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