Improve Non-Graph Matching Feature-Based Face Recognition Performance By Using a Multi-Stage Matching Strategy
Computing Sciences and Computer Engineering
© Springer International Publishing Switzerland 2015. In this paper, a multi-stage matching strategy that determines the recognition result step by step is employed to improve the recognition performance of a non-graph matching feature-based face recognition. As the gallery size increases, correct correspondence of feature points between the probe image and training images becomes more and more difficult so that the recognition accuracy degrades gradually. To deal with the recognition degradation problem, we propose a multi-stage matching strategy for the non-graph matching feature-based method. Instead of finding the best match, each step picks out one half of the best matching candidates and removes the other half. The behavior of picking and removing repeats until the number of the remaining candidates is small enough to decide the final result. The experimental result shows that with the multi-stage matching strategy, the recognition performance is remarkably improved. Moreover, the improvement level also increases with the gallery size.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
(2015). Improve Non-Graph Matching Feature-Based Face Recognition Performance By Using a Multi-Stage Matching Strategy. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 248-257.
Available at: https://aquila.usm.edu/fac_pubs/17923