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Multi-algorithmic Fusion for Reliable Age and Gender Estimation from Face Images

Terhörst, Philipp and Huber, Marco and Kolf, Jan Niklas and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan (2019):
Multi-algorithmic Fusion for Reliable Age and Gender Estimation from Face Images.
FUSION 2019 - 22nd International Conference on Information Fusion, Ottawa, Canada, 02.-05. July, 2019, [Conference or Workshop Item]

Abstract

Automated estimation of demographic attributes, such as gender and age, became of great importance for many potential applications ranging from forensics to social media. Although previous works reported performances that closely match human level. These solutions lack of human intuition that allows human beings to state the confidences of their predictions. While the human intuition subconsciously considers surrounding conditions or the lack of experience in a certain task, current algorithmic solutions tend to mispredict with high confidence scores. In this work, we propose a multi-algorithmic fusion approach for age and gender estimation that is able to accurately state the model’s prediction reliability. Our solution is based on stochastic forward passes through a dropout-reduced neural network ensemble. By utilizing multiple stochastic forward passes combined from the neural network ensemble, the centrality and dispersion of these predictions are used to derive a confidence statement about the prediction. Our experiments were conducted on the Adience benchmark.We showed that the proposed solution reached and exceeded state-of-the-art performance for the age and gender estimation tasks. Further, we demonstrated that the reliability statements of the predictions of our proposed solution capture challenging conditions and underrepresented training samples.

Item Type: Conference or Workshop Item
Erschienen: 2019
Creators: Terhörst, Philipp and Huber, Marco and Kolf, Jan Niklas and Damer, Naser and Kirchbuchner, Florian and Kuijper, Arjan
Title: Multi-algorithmic Fusion for Reliable Age and Gender Estimation from Face Images
Language: English
Abstract:

Automated estimation of demographic attributes, such as gender and age, became of great importance for many potential applications ranging from forensics to social media. Although previous works reported performances that closely match human level. These solutions lack of human intuition that allows human beings to state the confidences of their predictions. While the human intuition subconsciously considers surrounding conditions or the lack of experience in a certain task, current algorithmic solutions tend to mispredict with high confidence scores. In this work, we propose a multi-algorithmic fusion approach for age and gender estimation that is able to accurately state the model’s prediction reliability. Our solution is based on stochastic forward passes through a dropout-reduced neural network ensemble. By utilizing multiple stochastic forward passes combined from the neural network ensemble, the centrality and dispersion of these predictions are used to derive a confidence statement about the prediction. Our experiments were conducted on the Adience benchmark.We showed that the proposed solution reached and exceeded state-of-the-art performance for the age and gender estimation tasks. Further, we demonstrated that the reliability statements of the predictions of our proposed solution capture challenging conditions and underrepresented training samples.

Uncontrolled Keywords: Biometrics Biometric fusion Face recognition
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Interactive Graphics Systems
20 Department of Computer Science > Mathematical and Applied Visual Computing
Event Title: FUSION 2019 - 22nd International Conference on Information Fusion
Event Location: Ottawa, Canada
Event Dates: 02.-05. July, 2019
Date Deposited: 09 Apr 2020 12:17
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