CNN and LSTM Based Facial Expression Analysis Model for a Humanoid Robot

Tzuu Hseng S. Li, Ping Huan Kuo, Ting Nan Tsai, Po Chien Luan

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)


Robots must be able to recognize human emotions to improve the human-robot interaction (HRI). This study proposes an emotion recognition system for a humanoid robot. The robot is equipped with a camera to capture users' facial images, and it uses this system to recognize users' emotions and responds appropriately. The emotion recognition system, based on a deep neural network, learns six basic emotions: happiness, anger, disgust, fear, sadness, and surprise. First, a convolutional neural network (CNN) is used to extract visual features by learning on a large number of static images. Second, a long short-term memory (LSTM) recurrent neural network is used to determine the relationship between the transformation of facial expressions in image sequences and the six basic emotions. Third, CNN and LSTM are combined to exploit their advantages in the proposed model. Finally, the performance of the emotion recognition system is improved by using transfer learning, that is, by transferring knowledge of related but different problems. The performance of the proposed system is verified through leave-one-out cross-validation and compared with that of other models. The system is applied to a humanoid robot to demonstrate its practicability for improving the HRI.

Original languageEnglish
Article number8760246
Pages (from-to)93998-94011
Number of pages14
JournalIEEE Access
Publication statusPublished - 2019

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Materials Science
  • General Engineering


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