A Deep Feature Fusion Network Based on Multiple Attention Mechanisms for Joint Iris-Periocular Biometric Recognition

Abstract

Joint iris-periocular recognition based on feature fusion can overcome some inherent drawbacks of unimodal biometrics, but most of the prior works are limited by conventional feature extraction approaches and fixed fusion schemes. To achieve more accurate and adaptive recognition, an end-to-end deep feature fusion network for joint iris-periocular recognition is proposed in this paper. Multiple attention mechanisms including self-attention and co-attention mechanisms are integrated into the network. Specifically, two forms of self-attention mechanisms, spatial attention and channel attention, are inserted into the feature extraction module, aiming to effectively learn the most important features and suppress unnecessary ones. Also, co-attention mechanism is introduced in the feature fusion module, which can adaptively fuse features to obtain more representative iris-periocular features. Additionally, in order to further enhance the discriminative power of the learned features, the proposed network is trained with a joint supervision of softmax loss and center loss. On two publicly available datasets, the proposed network with a small number of parameters outperforms unimodal biometrics and several iris-periocular recognition approaches.