Sparse representation classification via fast matching pursuit for face recognition

Citation:
Abdel-Sayed, M. M., A. Khattab, and M. F. Abu-Elyazeed, "Sparse representation classification via fast matching pursuit for face recognition", 2017 Japan-Africa Conference on Electronics, Communications and Computers (JAC-ECC), Alexandria, Egypt, 2017.

Abstract:

Face recognition is a widely studied pattern recognition problem. One of the most crucial components of face recognition problems is classification. Sparse representation-based classification (SRC) has been recently proposed to considerably improve the classification performance by using the compressed sensing theory. However, SRC utilizes ℓ1minimization for recovery. Despite being optimal, ℓ1minimization is computationally expensive, and hence, not applicable in real-time applications. In this paper, we present the Fast Matching Pursuit (FMP) which is a compressed sensing recovery algorithm that results in a recognition time that is only 4% to 10% of that of ℓ1minimization and approximately half the time of existing related matching pursuit approaches. This significant speedup does not come at the expense of any degradation in the recognition rate.

pages:

103-106

Tourism