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Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction

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Proceedings of the Future Technologies Conference (FTC) 2019 (FTC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1070))

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Abstract

In just a few years, online dating has become the dominant way that young people meet to date, making the deceptively error-prone task of picking good dating profile photos vital to a generation’s ability to form romantic connections. Until now, artificial intelligence approaches to Dating Photo Impression Prediction (DPIP) have been very inaccurate, unadaptable to real-world application, and have only taken into account a subject’s physical attractiveness. To that effect, we propose Photofeeler-D3 - the first convolutional neural network as accurate as 10 human votes for how smart, trustworthy, and attractive the subject appears in highly variable dating photos. Our “attractive” output is also applicable to Facial Beauty Prediction (FBP), making Photofeeler-D3 state-of-the-art for both DPIP and FBP. We achieve this by leveraging Photofeeler’s Dating Dataset (PDD) with over 1 million images and tens of millions of votes, our novel technique of voter modeling, and cutting-edge computer vision techniques.

A. Kalra—This author served as a research partner for the duration of this project but is not an employee at Photofeeler Inc. For any inquiries related to Photofeeler Inc. please email ben@photofeeler.com.

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Author information

Authors and Affiliations

  1. Photofeeler Inc, Denver, CO, USA

    Agastya Kalra & Ben Peterson

Authors
  1. Agastya Kalra
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  2. Ben Peterson
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Corresponding author

Correspondence to Agastya Kalra .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Kalra, A., Peterson, B. (2020). Photofeeler-D3: A Neural Network with Voter Modeling for Dating Photo Impression Prediction. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Proceedings of the Future Technologies Conference (FTC) 2019. FTC 2019. Advances in Intelligent Systems and Computing, vol 1070. Springer, Cham. https://doi.org/10.1007/978-3-030-32523-7_13

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