RGB-D Based Pose-Invariant Face Recognition Via Attention Decomposition Module

Face recognition has recently achieved remarkable performance with the help of deep learning networks, but there is still a domain gap between frontal and profile face recognition. Generally speaking, we utilize pose-invariant face recognition methods or incorporate additional depth information to handle pose variations. However, huge backbones or multiple models are often used in RGB-D face recognition methods, which makes them hard to be applied in edge devices. In this work, we propose a RGB-D based pose-invariant face recognition model which is light enough to meet the demands of edge devices. First, we use the attention decomposition mechanism to decompose the mixed feature maps into pose- and identity-related features layer by layer. Second, a continuously indexed domain adaptation and a multi-task training framework are applied to our proposed model to decorrelate these two components. Third, we design our proposed model by embedded convolution neural network (eCNN) architecture to reduce parameters and operations. Finally, we evaluate our proposed method on the public KincetFaceDB. The Rank-1 recognition rate of our proposed method reaches 98.23% on KincetFaceDB, which is 0.13% superior to that of [1]. The parameters of our proposed eCNN model is 0.58M, which is 98.47% lower than that of [1].