中文

Journal of Applied Sciences ›› 2021, Vol. 39 ›› Issue (3): 357-356.doi: 10.3969/j.issn.0255-8297.2021.03.002

• Column of CCF NCCA 2020 • Previous Articles    

Fully Expression Frame Localization and Recognition Based on Dynamic Face Image Sequences

SIMA Yi1,3, YI Jizheng1,2,3, CHEN Aibin1,2,3, ZHOU Mengna1,3   

  1. 1. Institute of Artificial Intelligence Application, Central South University of Forestry and Technology, Changsha 410004, Hunan, China;
    2. Hunan Key Laboratory of Intelligent Logistics Technology, Central South University of Forestry and Technology, Changsha 410004, Hunan, China;
    3. School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410004, Hunan, China
  • Received:2020-08-20 Published:2021-06-08

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Abstract: Considering that the evolution of facial expressions is a continuous process, compared to static images, dynamic image sequences are more suitable as the research objects for facial expression recognition. This paper proposes a sequence frame positioning model based on embedding network. The pre-trained Inception ResNet v1 network extracts the feature vectors of each frame, and then calculates the Euclidean distance between the feature vectors to position the complete frame with the maximum expression intensity, so a standardized facial expression sequences are obtained. In order to further verify the accuracy of the positioning model, we adopt VGG16 network and ResNet50 network to perform facial expression recognition on the positioned complete frame, respectively. Experiments were conducted on the CK+ and MMI facial expression databases. The average accuracy of the sequential frame positioning model proposed in this paper reached 98.31% and 98.08%, respectively. As using the VGG16 network and ResNet50 network to perform expression recognition on the positioned complete frame, the recognition accuracies on the two databases reached 96.32% and 96.5%, 87.23% and 87.88%, respectively. These experimental results show that the proposed model can pick up the complete frame from the facial expression sequence accurately and achieve better performance on facial expression recognition as well.

Key words: facial expression sequence, embedding network, fully frame position, feature vector, facial expression recognition

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