Facial expression recognition with FRR‐CNN
Siyue Xie, Haifeng Hu · Electronics Letters · 2017
Feature redundancy‐reduced convolutional neural network (FRR‐CNN) is proposed to address the problem of facial expression recognition. Different from traditional CNN, convolutional kernels of FRR‐CNN is induced to be divergent by presenting a more discriminative mutual difference among feature maps of the same layer, which results in generating less redundant features and yields a more compact representation of an image. Furthermore, the transformation‐invariant pooling strategy is used to extract representative features cross‐transformations. Extensive experiments are conducted on two public facial expression databases and the obtained results demonstrate the efficiency of FRR‐CNN comparing with the state‐of‐the‐art expression recognition methods.