Visual search and application using deep learning (Age group classification with convolution neural network)

The consistency of processing input facial images to automatically estimating human age has generally been found to be poor. Facial image classification is an approach to classify face images into a few predefined age groups. Predicting age group from face images acquired in unconstrained conditions...

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Main Author: Low, Benjamin
Other Authors: Yap Kim Hui
Format: Final Year Project
Language:English
Published: 2018
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Online Access:http://hdl.handle.net/10356/74924
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-749242023-07-07T18:06:08Z Visual search and application using deep learning (Age group classification with convolution neural network) Low, Benjamin Yap Kim Hui School of Electrical and Electronic Engineering DRNTU::Engineering The consistency of processing input facial images to automatically estimating human age has generally been found to be poor. Facial image classification is an approach to classify face images into a few predefined age groups. Predicting age group from face images acquired in unconstrained conditions have been a challenging and important task in many modern applications. With the rise of social media sites, this has become relevant to an increasing amount of applications. Nevertheless, performance of existing methods with manually-designed features on in-the-wild benchmarks are still lacking in this area, as compared to the improving results in performance reported for the related task of facial recognition. In this paper, a deep CNN model that was trained for face recognition task is used to estimate the age information on the IMDB-WIKI database. To this end, a proposed simple CNN architecture can be used even when the amount of learning data is limited. Our experiments illustrate the effectiveness of different models for age group estimation in the wild. Further studies will be evaluated on the method used on the recent IMDB-WIKI benchmark for age group classification and compared it to other state of the art methods. Bachelor of Engineering 2018-05-25T01:23:54Z 2018-05-25T01:23:54Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/74924 en Nanyang Technological University 47 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Engineering
spellingShingle DRNTU::Engineering
Low, Benjamin
Visual search and application using deep learning (Age group classification with convolution neural network)
description The consistency of processing input facial images to automatically estimating human age has generally been found to be poor. Facial image classification is an approach to classify face images into a few predefined age groups. Predicting age group from face images acquired in unconstrained conditions have been a challenging and important task in many modern applications. With the rise of social media sites, this has become relevant to an increasing amount of applications. Nevertheless, performance of existing methods with manually-designed features on in-the-wild benchmarks are still lacking in this area, as compared to the improving results in performance reported for the related task of facial recognition. In this paper, a deep CNN model that was trained for face recognition task is used to estimate the age information on the IMDB-WIKI database. To this end, a proposed simple CNN architecture can be used even when the amount of learning data is limited. Our experiments illustrate the effectiveness of different models for age group estimation in the wild. Further studies will be evaluated on the method used on the recent IMDB-WIKI benchmark for age group classification and compared it to other state of the art methods.
author2 Yap Kim Hui
author_facet Yap Kim Hui
Low, Benjamin
format Final Year Project
author Low, Benjamin
author_sort Low, Benjamin
title Visual search and application using deep learning (Age group classification with convolution neural network)
title_short Visual search and application using deep learning (Age group classification with convolution neural network)
title_full Visual search and application using deep learning (Age group classification with convolution neural network)
title_fullStr Visual search and application using deep learning (Age group classification with convolution neural network)
title_full_unstemmed Visual search and application using deep learning (Age group classification with convolution neural network)
title_sort visual search and application using deep learning (age group classification with convolution neural network)
publishDate 2018
url http://hdl.handle.net/10356/74924
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