TNNL: A novel image dimensionality reduction method for face image recognition

Face image recognition technology plays an important role in biometric recognition field. Among the present face recognition methods, the method based on subspace learning have aroused wide concern due to their favorable properties, such as convenience for computation and effectiveness for identification. However, existing methods based on subspace learning are out of work when the sample-specific corruptions and outliers come along. To solve this problem, we build a novel model for face image recognition named truncated nuclear norm on low rank discriminant embedding (TNNL). The TNNL can mitigate the negative impact of noise and enhance the discriminability of features. Furthermore, we propose two iterative algorithms to extract the robust low dimensional image feature. To testify the effectiveness and robustness of TNNL, we conduct experiments on two benchmark face image databases for low dimensional feature extraction. The experimental results show that TNNL is better than the existing methods. (C) 2021 Elsevier Inc. All rights reserved.

Automated summary

The face image recognition technology based on subspace learning has limitations, so we propose the TNNL model to address sample-specific corruptions and outliers, and experimental results demonstrate its effectiveness.