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In this paper, we propose a computational efficient end-to-end training deepneural network (CEDNN) model and spatial attention maps based on differenceimages. Firstly, the difference image is generated by image processing. Thenfive binary images of difference images are obtained using differentthresholds, which are used as spatial attention maps. We use group convolutionto reduce model complexity. Skip connection and $\text{1}\times \text{1}$convolution are used to ensure good performance even if the network model isnot deep. As an input, spatial attention map can be selectively fed into theinput of each block. The feature maps tend to focus on the parts that arerelated to the target task better. In addition, we only need to adjust theparameters of classifier to train different numbers of AU. It can be easilyextended to varying datasets without increasing too much computation. A largenumber of experimental results show that the proposed CEDNN is obviously betterthan the traditional deep learning method on DISFA+ and CK+ datasets. Afteradding spatial attention maps, the result is better than the most advanced AUdetection method. At the same time, the scale of the network is small, therunning speed is fast, and the requirement for experimental equipment is low.

Computational efficient deep neural network with difference attention maps for facial action unit detection