Name
Title | ||
|---|---|---|
Cascading global and local features for face recognition using support vector machines and local ternary patterns |
Abstract | ||
|---|---|---|
This study analyzes the effectiveness of the global (the whole face) and local (regions of eyes, nose, and mouth) features for face recognition. Features describing human faces are encoded in local ternary patterns. The two-class support vector machine is used as the supervised learning algorithm for training recognition models. In the recognition process, recognition modes based on the global features and local features are cascaded. For identifying a face image, the local features are used iteratively for filtering out candidates that can not be clearly identified by the global features, until the one with highest possibility is concluded. The experimental results show that cascading the recognition models of global and local features obtains better classification accuracy than the single classification process. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICInfA.2017.8078942 | 2017 IEEE International Conference on Information and Automation (ICIA) |
Keywords | Field | DocType |
Face recognition,feature reduction,texture,local ternary pattern,support vector machine | Local ternary patterns,Facial recognition system,Pattern recognition,Three-dimensional face recognition,Computer science,Support vector machine,Filter (signal processing),Haar-like features,Feature extraction,Feature (machine learning),Artificial intelligence | Conference |
ISBN | Citations | PageRank |
978-1-5386-3155-3 | 0 | 0.34 |
References | Authors | |
4 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jia-Ching Jang Jian | 1 | 0 | 0.34 |
| Chih-Hung Wu | 2 | 153 | 23.29 |
| Chih-Chin Lai | 3 | 448 | 26.53 |
| Shing-Tai Pan | 4 | 104 | 13.46 |
| Shie-Jue Lee | 5 | 48 | 5.11 |
| Chen-Sen Ouyang | 6 | 157 | 17.15 |