Surrogate network-based sparseness hyper-parameter optimization for deep expression recognition

For facial expression recognition, the sparseness constraints of the features or weights can improve the generalization ability of a deep network. However, the optimization of the hyper-parameters in fusing different sparseness strategies demands much computation, when the traditional gradient-based algorithms are used. In this work, an iterative framework with surrogate network is proposed for the optimization of hyper-parameters in fusing different sparseness strategies. In each iteration, a network with significantly smaller model complexity is fitted to the original large network based on four Euclidean losses, where the hyper-parameters are optimized with heuristic optimizers. Since the surrogate network uses the same deep metrics and embeds the same hyper-parameters as the original network, the optimized hyper-parameters are then used for the training of the original deep network in the next iteration. While the performance of the proposed algorithm is justified with a tiny model, i.e. LeNet on the FER2013 database, our approach achieved competitive performances on six publicly available expression datasets, i.e., FER2013, CK+, Oulu-CASIA, MMI, AFEW and AffectNet.