Variational Feature Representation-based Classification for face recognition with single sample per person

• Variational feature is represented by variation matrix with the reference of the gallery set.
• Normal feature is close to the corresponding ideal sample in the gallery set.
• Both normal feature and probe sample are used to make VFRC robust and effective for SSPP scenario.
• Experimental results show VFRC possesses higher recognition rate and lower computational cost.

The single sample per person (SSPP) problem is of great importance for real-world face recognition systems. In SSPP scenario, there is always a large gap between a normal sample enrolled in the gallery set and the non-ideal probe sample. It is a crucial step for face recognition with SSPP to bridge the gap between the ideal and non-ideal samples. For this purpose, we propose a Variational Feature Representation-based Classification (VFRC) method, which employs the linear regression model to fit the variational information of a non-ideal probe sample with respect to an ideal gallery sample. Thus, a corresponding normal feature, which reserve the identity information of the probe sample, is obtained. A combination of the normal feature and the probe sample is used, which makes VFRC method more robust and effective for SSPP scenario. The experimental results show that VFRC method possesses higher recognition rate than other related face recognition methods.