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

: Terhörst, Philipp; Huber, Marco; Kolf, Jan Niklas; Damer, Naser; Kirchbuchner, Florian; Kuijper, Arjan

International Society of Information Fusion -ISIF-; Institute of Electrical and Electronics Engineers -IEEE-:
22th International Conference on Information Fusion, FUSION 2019 : 2-5 July 2019, Ottawa, Canada
Piscataway, NJ: IEEE, 2019
ISBN: 978-0-9964527-8-6
ISBN: 978-1-7281-1840-6
8 pp.
International Conference on Information Fusion (FUSION) <22, 2019, Ottawa>
Conference Paper
Fraunhofer IGD ()
CRISP; Lead Topic: Smart City; Lead Topic: Visual Computing as a Service; Research Line: Computer vision (CV); biometrics; biometric fusion; face recognition

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.