We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Research on facial expression recognition algorithm based on improved MobileNetV3.
- Authors
Jiang, Bin; Li, Nanxing; Cui, Xiaomei; Zhang, Qiuwen; Zhang, Huanlong; Li, Zuhe; Liu, Weihua
- Abstract
Aiming at the problem that face images are easily interfered by occlusion factors in uncontrollable environments, and the complex structure of traditional convolutional neural networks leads to low expression recognition rates, slow network convergence speed, and long network training time, an improved lightweight convolutional neural network is proposed for facial expression recognition algorithm. First, the dilation convolution is introduced into the shortcut connection of the inverted residual structure in the MobileNetV3 network to expand the receptive field of the convolution kernel and reduce the loss of expression features. Then, the channel attention mechanism SENet in the network is replaced by the two-dimensional (channel and spatial) attention mechanism SimAM introduced without parameters to reduce the network parameters. Finally, in the normalization operation, the Batch Normalization of the backbone network is replaced with Group Normalization, which is stable at various batch sizes, to reduce errors caused by processing small batches of data. Experimental results on RaFD, FER2013, and FER2013Plus face expression data sets show that the network reduces the training times while maintaining network accuracy, improves network convergence speed, and has good convergence effects.
- Subjects
CONVOLUTIONAL neural networks; FACIAL expression; BATCH processing; ALGORITHMS
- Publication
EURASIP Journal on Image & Video Processing, 2024, Vol 2024, Issue 1, p1
- ISSN
1687-5176
- Publication type
Article
- DOI
10.1186/s13640-024-00638-z