Name
Abstract | ||
|---|---|---|
We present an approach for the human face reconstruction from a single frontal image for the use in forensic anthropology when the subject’s age and gender is known. In our approach we build a database of several depth images per each age and gender group pair, marked with facial landmarks. To reconstruct a 3D facial model from an unknown frontal image we search the most similar face in the depth database based on the automatically detected landmarks and assign its depth to the model. In the evaluation part, we compared our approach to a recent automatic convolutional neural network based algorithm and a semi-automatic approach, where landmarks are required to be detected manually. In contrast to other tested approaches our algorithm can estimate all major components, such as eyes, nose and mouth, evenly. Thanks to the external depth database, it can also reconstruct human faces from images with partial facial occlusions and uneven lighting. Additionally, we have found that a single depth image provides a good approximation of the human face and a combination of multiple precomputed depth images has a little impact on the final 3D face reconstruction result. Speed measurements show that our algorithm provides a quick and a fully automatic way to reconstruct a human face from a single frontal image for the use in forensic anthropology. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-018-6869-5 | Multimedia Tools and Applications |
Keywords | Field | DocType |
Face reconstruction, Single photo reconstruction, Depth image database, Frontal image, Forensic anthropology | Computer vision,Pattern recognition,Convolutional neural network,Computer science,Forensic anthropology,Artificial intelligence | Journal |
Volume | Issue | ISSN |
79 | 5-6 | 1573-7721 |
Citations | PageRank | References |
0 | 0.34 | 26 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zuzana Ferkova | 1 | 3 | 1.11 |
| Petra Urbanova | 2 | 6 | 1.49 |
| Dominik Černý | 3 | 0 | 0.34 |
| Marek Žuži | 4 | 0 | 0.34 |
| Petr Matula | 5 | 94 | 14.04 |