Multi-Region Ensemble Convolutional Neural Networks for High Accuracy Age Estimation

Yiliang Chen, Zichang Tan, Alex Po Leung, Jun Wang, Jianguo Zhang

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

In real life, telling a person’s age from his/her face, we tend to look at his/her whole face first and then focus on certain important regions like eyes. After that we will focus on each particular facial feature individually like the nose or the mouth so that we can decide on the age of the person. Similarly, in this paper, we propose a new framework for age estimation, which is based on human face sub-regions. Each sub-network in our framework is made up of two input images from human facial regions. One of them is the global face, and the other one is a vital sub-component. Then, we combine predictions from different sub-regions so as to make full use of various opinions from different regions based on a majority voting method. We call our framework Multi-Region Network Prediction Ensemble (MRNPE) and evaluate our approach based on two popular public datasets: MORPH Album II and Cross Age Celebrity Dataset (CACD). Experiments show that our method outperforms the existing state-of-the-art age estimation methods by a significant margin. The Mean Absolute Errors (MAE) of age estimation are dropped from 3.03 to 2.73 years on the MORPH Album II and 4.79 to 4.40 years on the CACD.
Original languageEnglish
Publication statusPublished - 2017
EventBritish Machine Vision Conference - Imperial College London, London, United Kingdom
Duration: 4 Sep 20177 Sep 2017
https://bmvc2017.london

Conference

ConferenceBritish Machine Vision Conference
Abbreviated titleBMVC 2017
CountryUnited Kingdom
CityLondon
Period4/09/177/09/17
Internet address

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Neural networks
Experiments

Cite this

Chen, Y., Tan, Z., Po Leung, A., Wang, J., & Zhang, J. (2017). Multi-Region Ensemble Convolutional Neural Networks for High Accuracy Age Estimation. Paper presented at British Machine Vision Conference, London, United Kingdom.
Chen, Yiliang ; Tan, Zichang ; Po Leung, Alex ; Wang, Jun ; Zhang, Jianguo. / Multi-Region Ensemble Convolutional Neural Networks for High Accuracy Age Estimation. Paper presented at British Machine Vision Conference, London, United Kingdom.
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abstract = "In real life, telling a person’s age from his/her face, we tend to look at his/her whole face first and then focus on certain important regions like eyes. After that we will focus on each particular facial feature individually like the nose or the mouth so that we can decide on the age of the person. Similarly, in this paper, we propose a new framework for age estimation, which is based on human face sub-regions. Each sub-network in our framework is made up of two input images from human facial regions. One of them is the global face, and the other one is a vital sub-component. Then, we combine predictions from different sub-regions so as to make full use of various opinions from different regions based on a majority voting method. We call our framework Multi-Region Network Prediction Ensemble (MRNPE) and evaluate our approach based on two popular public datasets: MORPH Album II and Cross Age Celebrity Dataset (CACD). Experiments show that our method outperforms the existing state-of-the-art age estimation methods by a significant margin. The Mean Absolute Errors (MAE) of age estimation are dropped from 3.03 to 2.73 years on the MORPH Album II and 4.79 to 4.40 years on the CACD.",
author = "Yiliang Chen and Zichang Tan and {Po Leung}, Alex and Jun Wang and Jianguo Zhang",
note = "This work was supported by the Macau Science and Technology Development Fund of (No. 019/2014/A1, No. 112/2014/A3), the National Key Research and Development Plan (Grant No. 2016YFC0801002), the Chinese National Natural Science Foundation Projects ]61502491, ]61473291, ]61572501, ]61572536, NVIDIA GPU donation program and AuthenMetric R&D Funds.; British Machine Vision Conference, BMVC 2017 ; Conference date: 04-09-2017 Through 07-09-2017",
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Chen, Y, Tan, Z, Po Leung, A, Wang, J & Zhang, J 2017, 'Multi-Region Ensemble Convolutional Neural Networks for High Accuracy Age Estimation' Paper presented at British Machine Vision Conference, London, United Kingdom, 4/09/17 - 7/09/17, .

Multi-Region Ensemble Convolutional Neural Networks for High Accuracy Age Estimation. / Chen, Yiliang ; Tan, Zichang ; Po Leung, Alex ; Wang, Jun; Zhang, Jianguo.

2017. Paper presented at British Machine Vision Conference, London, United Kingdom.

Research output: Contribution to conferencePaper

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T1 - Multi-Region Ensemble Convolutional Neural Networks for High Accuracy Age Estimation

AU - Chen, Yiliang

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AU - Po Leung, Alex

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AU - Zhang, Jianguo

N1 - This work was supported by the Macau Science and Technology Development Fund of (No. 019/2014/A1, No. 112/2014/A3), the National Key Research and Development Plan (Grant No. 2016YFC0801002), the Chinese National Natural Science Foundation Projects ]61502491, ]61473291, ]61572501, ]61572536, NVIDIA GPU donation program and AuthenMetric R&D Funds.

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N2 - In real life, telling a person’s age from his/her face, we tend to look at his/her whole face first and then focus on certain important regions like eyes. After that we will focus on each particular facial feature individually like the nose or the mouth so that we can decide on the age of the person. Similarly, in this paper, we propose a new framework for age estimation, which is based on human face sub-regions. Each sub-network in our framework is made up of two input images from human facial regions. One of them is the global face, and the other one is a vital sub-component. Then, we combine predictions from different sub-regions so as to make full use of various opinions from different regions based on a majority voting method. We call our framework Multi-Region Network Prediction Ensemble (MRNPE) and evaluate our approach based on two popular public datasets: MORPH Album II and Cross Age Celebrity Dataset (CACD). Experiments show that our method outperforms the existing state-of-the-art age estimation methods by a significant margin. The Mean Absolute Errors (MAE) of age estimation are dropped from 3.03 to 2.73 years on the MORPH Album II and 4.79 to 4.40 years on the CACD.

AB - In real life, telling a person’s age from his/her face, we tend to look at his/her whole face first and then focus on certain important regions like eyes. After that we will focus on each particular facial feature individually like the nose or the mouth so that we can decide on the age of the person. Similarly, in this paper, we propose a new framework for age estimation, which is based on human face sub-regions. Each sub-network in our framework is made up of two input images from human facial regions. One of them is the global face, and the other one is a vital sub-component. Then, we combine predictions from different sub-regions so as to make full use of various opinions from different regions based on a majority voting method. We call our framework Multi-Region Network Prediction Ensemble (MRNPE) and evaluate our approach based on two popular public datasets: MORPH Album II and Cross Age Celebrity Dataset (CACD). Experiments show that our method outperforms the existing state-of-the-art age estimation methods by a significant margin. The Mean Absolute Errors (MAE) of age estimation are dropped from 3.03 to 2.73 years on the MORPH Album II and 4.79 to 4.40 years on the CACD.

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Chen Y, Tan Z, Po Leung A, Wang J, Zhang J. Multi-Region Ensemble Convolutional Neural Networks for High Accuracy Age Estimation. 2017. Paper presented at British Machine Vision Conference, London, United Kingdom.