From face identification to emotion recognition

Xin Chang; Władysław Skarbek

doi:10.1117/12.2536735

6 November 2019 From face identification to emotion recognition

Xin Chang, Władysław Skarbek

Proceedings Volume 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019; 111760K (2019) https://doi.org/10.1117/12.2536735
Event: Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 2019, Wilga, Poland

Abstract

This paper aims to explore the practicality of transfer learning regarding to the emotion recognition task. We present superior performance of the transfer learning from the face identification, compared with the solutions of train-from-scratch feed-forward deep neural networks and general transfer learning from object classifications. We illustrate that the better adaptation of source domain can help with the initialization of the network, providing more efficient learning from the target training samples. In such way even network with complex architecture can overcome over-fitting problems thus having better results than other solutions can do having the same amount of training data. We discuss the detailed training strategies to the get best performance of such transfer leaning using fine-tuning mechanisms on the classical VGG-16 architecture network based on the public accessible FER2013 emotion database.

Citation Download Citation

Xin Chang and Władysław Skarbek "From face identification to emotion recognition", Proc. SPIE 11176, Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2019, 111760K (6 November 2019); https://doi.org/10.1117/12.2536735

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