Face Feature Weighted Fusion Based on Fuzzy Membership Degree for Video Face Recognition.
This paper proposes a new video face recognition (FR) method that is designed for significantly improving FR via adaptive fusion of multiple face features (belonging to the same subject) acquired from a face sequence of video frames. In this paper, we derive an upper bound for recognition error arising from the proposed weighted feature fusion to justify theoretically its effectiveness for recognition from videos. In addition, in order to compute the optimal weights of face features to be fused, we develop a novel weight determination solution based on fuzzy membership function and quality measurement for face images. Using four public video databases, the effectiveness of the proposed method has been successfully evaluated under the conditions that are similar to those in real-world video FR applications. Furthermore, our method is simple and straightforward to implement.
Jae Young Choi
K N
KN
Plataniotis
Yong Man Ro