Name
Abstract | ||
|---|---|---|
Modern Face Recognition (FR) systems have to cope with high intra-class variability. Face is a 3D object with wide variability in appearances from each pose. This, along with other causes like luminance variation, becomes one of the main reasons for the high variability. While incremental learning is an unsuited approach for most scenarios, techniques proposed to deal with this issue are often of enormous complexity during training phase and require computational power during operation that is scarcely credible for compact devices. This paper proposes a novel technique where pose specific classification system has been used to attain better classification with low computational cost. State of the art performance measures have been used to assess the validity. Results demonstrate the effectiveness of our pose specific classification system. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11042-020-08872-z | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Face recognition, Low computational cost, Pose specific classification, Classification | Journal | 80 |
Issue | ISSN | Citations |
20 | 1380-7501 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Muhammad Asad | 1 | 28 | 10.57 |
| Ayyaz Hussain | 2 | 0 | 1.01 |
| Usama Mir | 3 | 0 | 0.34 |