Recent advances in face recognition are mostly based on deep methods that require large datasets for training. This paper presents a novel method that combines Gabor-features, feature selection and kernel selection to achieve comparable performance on smaller datasets.

The paper compares different feature selection methods in this context.
The problem tackled in this paper is achieving accurate face recognition with limited computational resources. By “limited" computational resources we mean low computational power (i.e. memory, CPU ops) during both system training and evaluation. Noted that we are not competing against deep learning systems in term of accuracy but we provided a middle ground between hand-coded fast feature extraction and learning based deep learning in terms of both speed and accuracy.
To achieve this goal, we propose “kernel selection" as the main method to reduce the dimensionality of the classification problem faced by the final classifier in the FR system. Kernel selection is the process of eliminating less important Gabor kernels for classification while keeping the level of accuracy achievable. Kernel selection differs from traditional feature selection in measuring the value of complete kernels consisting of several features together. Because of its structured nature, Kernel selection has the advantage of eliminating the need to evaluate complete Gabor kernels reducing the computational cost of the system compared with traditional feature selection methods.