Paper Info
Title
An Hybrid Online Training Face Recognition System Using Pervasive Intelligence Assisted Semantic Information.
Abstract
In face recognition, the large sizes of training databases can place a heavy burden on computing resources and may produce unsatisfactory results due to significant amount of irrelevant features for target screening. We adopt the technology of wireless sensor networks by storing semantic information in wireless tags to assist grouping of candidates. The semantic information of nearby people such as gender and race is provided to the robot and help it narrows its search to a smaller subset of the database. Hence the face recognizer can be simplified by training the selected subset samples that makes online training possible. Furthermore, the feature space can be constantly adjusted benefiting from online training to distinguish faces with higher accuracy and the resolution of training samples can also be adjusted based on the camera and target distance. In order to further improve the correct rate, permutation post processing has been employed. The proposed hybrid approach has been validated in experiments with a promising low error rate. Compared to other face recognition systems, our system is better suited to work on a human-machine interactive robot which needs to detect targets under different illumination conditions and from different distances.
Year
DOI
Venue
2016
10.1007/978-3-319-40379-3_37
Lecture Notes in Artificial Intelligence
Keywords
Field
DocType
Face recognition,Semantic information,Pervasive intelligence
Computer vision,Facial recognition system,Feature vector,Wireless,Computer science,Word error rate,Permutation,Semantic information,Artificial intelligence,Robot,Wireless sensor network,Machine learning
Conference
Volume
ISSN
Citations 
9716
0302-9743
0
PageRank 
References 
Authors
0.34
6
4
Name
Order
Citations
PageRank
Dongfei Xue101.69
Yongqiang Cheng213329.99
Ping Jiang3529.06
Martin Walker4626.08