Fusion facial semantic feature and incremental learning mechanism for efficient face recognition

Efficient face recognition can realize fast and accurate face recognition and make it widely used in essential fields such as human–computer interaction and access control. At present, there are many face recognition methods whose recognition rate can reach high accuracy, but the training of the model and the recognition of samples take much time, which leads to insufficient real-time performance. This paper designs a fusion facial semantic feature (FFSF) and an incremental learning mechanism (ILM) for efficient face recognition. FFSF feature is a fusion of facial contour features and facial semantic component features, which can extract contour features and interior features of facial organs (eyes, mouth, nose, and eyebrow) according to facial organs’ position. FFSF features can ensure that the extracted features are concentrated in the face’s most discriminative region, making the extracted features have good discriminative characteristics. Then, we use a clustering algorithm to construct a hierarchical incremental learning tree (HIL-Tree) with a hierarchical structure and use the HIL-Tree to implement the ILM. ILM achieves fast and accurate sample classification by retrieving the nodes in HIL-Tree, and the training samples can be directly added to the HIL-Tree by retrieval instead of rebuilding the HIL-Tree during the training process. Extensive experiments on several public data sets demonstrate the proposed efficient face recognition method’s excellent accuracy and efficiency.