Multi‐factor joint normalisation for face recognition in the wild

Abstract Face recognition has become very challenging in unconstrained conditions due to strong intra‐personal variations, such as large pose changes. Face normalisation can help to resolve these problems and effectively improve the face recognition performance in unconstrained conditions by converting non‐frontal faces to frontal ones. However, there are other complex facial variations in addition to pose, such as illumination and expression, which will also influence face recognition performance. The authors propose a well‐designed generative adversarial network‐based multi‐factor joint normalisation network (MFJNN) to normalise multiple factors simultaneously. First, a multi‐encoder generator and a feature fusion strategy are designed and implemented in the MFJNN to realise the joint normalisation of multiple factors in addition to pose. Second, a convolutional neural network‐based (CNN‐based) network is applied in the MFJNN, which allows the MFJNN to simultaneously realise image synthesis and facial representation learning. Moreover, an identity perceptive loss is introduced based on the CNN‐based network to produce reliable identity‐preserving features of the input face images. The experimental results demonstrate that the proposed method can synthesise multi‐factor normalisation results with identity preservation and effectively improve the face recognition performance.