Face recognition using ensemble statistical local descriptors

The use of data fusion can be of a enormous help in boosting classification performance. Feature fusion is a data fusion technique that is being considered in this study. The effect of fusing different feature descriptors extracted by using histogram-based local feature extraction algorithms on the performance of the face recognition problem is investigated. Feature fusion/concatenation of more than one generated feature descriptor is applied. The impact of fused two and three feature descriptors on the system perfor-mance is evaluated when the training set is limited to only one-shot per person. Extensive experiments are carried out using two well-known face databases. Comparisons are conducted among different algo-rithms for extraction of the local statistical feature descriptors of the face images. The obtained results show that feature fusion of the descriptors can significantly improve the performance with certain fea -ture descriptors.(c) 2023 Published by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study investigates the impact of feature fusion on face recognition performance by fusing different feature descriptors. The results show that fused feature descriptors can significantly improve performance, especially when the training set is limited.