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.
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From face identification to emotion recognition
Semantic Scholar · Computer Science · 2019
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.