Name
Abstract | ||
|---|---|---|
Low cost RGB-D sensors like Kinect and RealSense enable easy acquisition of both RGB (i.e., texture) and depth images of human faces. Many methods have been proposed to improve the RGB-to-RGB face matcher by fusing it with the Depth-to-Depth face matcher. Yet, few efforts have been devoted to the matching between RGB and Depth face images. In this paper, we propose two deep convolutional neural network (DCNN) based approaches to Depth-to-RGB face recognition, and compare their performance in terms of face verification accuracy. We further combine the Depth-to-RGB matcher with the RGB-to-RGB matcher via score-level fusion. Evaluation experiments on two databases demonstrate that matching depth to RGB does boost face verification accuracy. |
Year | Venue | Field |
|---|---|---|
2017 | CCBR | Face verification,Computer vision,Facial recognition system,Convolutional neural network,Computer science,Boosting (machine learning),Artificial intelligence,RGB color model,Deep learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Liu | 1 | 46 | 9.33 |
| Feixiang He | 2 | 0 | 0.34 |
| Qijun Zhao | 3 | 419 | 38.37 |
| Xiangdong Fei | 4 | 0 | 0.34 |