  Person identification (P-ID) under real unconstrained noisy environments is a
huge challenge. In multiple-feature learning with Deep Convolutional Neural
Networks (DCNNs) or Machine Learning method for large-scale person
identification in the wild, the key is to design an appropriate strategy for
decision layer fusion or feature layer fusion which can enhance discriminative
power. It is necessary to extract different types of valid features and
establish a reasonable framework to fuse different types of information. In
traditional methods, different persons are identified based on single modal
features to identify, such as face feature, audio feature, and head feature.
These traditional methods cannot realize a highly accurate level of person
identification in real unconstrained environments. The study aims to propose a
fusion module to fuse multi-modal features for person identification in real
unconstrained environments.
