Robust face recognition based on the wing loss and the $\ell_1$ regularization
This addresses robust face recognition for applications like security or surveillance, but it is incremental as it builds on existing sparse sampling techniques.
The paper tackles the problem of face recognition under high occlusion and damage by proposing a wing-constrained sparse coding model (WCSC) and its weighted version (WWCSC), achieving a very high recognition rate in complex situations as demonstrated on four facial databases.
In recent years, sparse sampling techniques based on regression analysis have witnessed extensive applications in face recognition research. Presently, numerous sparse sampling models based on regression analysis have been explored by various researchers. Nevertheless, the recognition rates of the majority of these models would be significantly decreased when confronted with highly occluded and highly damaged face images. In this paper, a new wing-constrained sparse coding model(WCSC) and its weighted version(WWCSC) are introduced, so as to deal with the face recognition problem in complex circumstances, where the alternating direction method of multipliers (ADMM) algorithm is employed to solve the corresponding minimization problems. In addition, performances of the proposed method are examined based on the four well-known facial databases, namely the ORL facial database, the Yale facial database, the AR facial database and the FERET facial database. Also, compared to the other methods in the literatures, the WWCSC has a very high recognition rate even in complex situations where face images have high occlusion or high damage, which illustrates the robustness of the WWCSC method in facial recognition.