Aiming at the problem of a large number of parameters and high time complexity caused by the current deep convolutional neural network models, an improved face alignment algorithm of a cascaded convolutional neural network (CCNN) is proposed from the network structure, random perturbation factor (shake), and data scale. The algorithm steps are as follows: 3 groups of lightweight CNNs are designed; the first group takes facial images with face frame as input, trains 3 CNNs in parallel, and weighted outputs the facial images with 5 facial key points (anchor points). Then, the anchor points and 2 different windows with a shake mechanism are used to crop out 10 partial images of human faces. The networks in the second group train 10 CNNs in parallel and every 2 networks’ weighted average and colocated a key point. Based on the second group of networks, the third group designed a smaller shake mechanism and windows, to achieve more fine-tuning. When training the network, the idea of parallel within groups and serial between groups is adopted. Experiments show that, on the LFPW face dataset, the improved CCNN in this paper is superior to any other algorithm of the same type in positioning speed, algorithm parameter amount, and test error.
High Speed Implementation of the Deformable Shape Tracking Face Alignment Algorithm
