With an aim of extracting robust facial features under pose variations, this paper presents two directional projections corresponding to extraction of vertical and horizontal local face image features. The matching scores computed from both horizontal and vertical features are subsequently fused at score level via an extreme learning machine that optimizes the total error rate for performance enhancement. In order to benchmark the performance, both the feature extraction and fusion results are compared with that of popular face recognition methods such as principal components analysis and linear discriminant analysis in terms of equal error rate and CPU time. Our empirical experiments using four data sets show encouraging results under considerable horizontal pose variations.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) through the Biometrics Research Engineering Center (BERC) at Yonsei University (Grant Number: R112002105090010 (2010) and R112002105080020 (2010)) .
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Cognitive Neuroscience
- Artificial Intelligence