Name
Abstract | ||
|---|---|---|
We present a real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner. The approach requires no extra hardware except for a generic webcamera. Eyeblink sequences often have a complex underlying structure. We formulate blink detection as inference in an undirected conditional graphical framework, and are able to learn a compact and efficient observation and transition potentials from data. For purpose of quick and accurate recognition of the blink behavior, eye closity, an easily-computed discriminative measure derived from the adaptive boosting algorithm, is developed, and then smoothly embedded into the conditional model. An extensive set of experiments are presented to show effectiveness of our approach and how it outperforms the cascaded Adaboost and HMM in task of eyeblink detection. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICCV.2007.4409068 | ICCV |
Keywords | Field | DocType |
face recognition,blink detection,undirected conditional graphical framework,cascaded adaboost,real-time liveness detection approach,generic webcamera,adaptive boosting algorithm,object detection,hidden markov models,eyeblink-based antispoofing,real time | Object detection,Facial recognition system,AdaBoost,Spoofing attack,Pattern recognition,Computer science,Speech recognition,Boosting (machine learning),Artificial intelligence,Hidden Markov model,Discriminative model,Liveness | Conference |
Volume | Issue | ISSN |
2007 | 1 | 1550-5499 E-ISBN : 978-1-4244-1631-8 |
ISBN | Citations | PageRank |
978-1-4244-1631-8 | 56 | 2.12 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gang Pan | 1 | 1501 | 123.57 |
| Lin Sun | 2 | 145 | 9.46 |
| Zhaohui Wu | 3 | 3121 | 246.32 |
| Shihong Lao | 4 | 2005 | 118.22 |