Distance approximation for two-phase test sample representation in face recognition

The two-phase test sample representation (TPTSR) scheme was proposed as a useful method for face recognition; however, the sample selection based on sparse representation in the first phase is not necessary. This is because the first phase only plays a role of course search in TPTSR, but the sparse representation method is suitable for fine classification. This paper proves that alternative nearest-neighbor selection criterions with higher efficiency can be used in the first phase of TPTSR without compromising the classification accuracy. Theoretical analysis and experimental results show that the original distance metric based on sparse representation in the first phase of the TPTSR can be approximated with a more straightforward metric while maintaining a comparable classification performance with the original TPTSR. Therefore, the computational load of the TPTSR can be greatly reduced.