Name
Abstract | ||
|---|---|---|
Face recognition is an increasingly popular method for user authentication. However, face recognition is susceptible to playback attacks. Therefore, a reliable way to detect malicious attacks is crucial to the robustness of the system. We propose and validate a novel physics-based method to detect images recaptured from printed material using only a single image. Micro-textures present in printed paper manifest themselves in the specular component of the image. Features extracted from this component allows a linear SVM classifier to achieve 2.2% False Acceptance Rate and 13% False Rejection Rate (6.7% Equal Error Rate). We also show that the classifier can be generalizable to contrast enhanced recaptured images and LCD screen recaptured images without re-training, demonstrating the robustness of our approach. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ISCAS.2010.5537866 | ISCAS |
Keywords | Field | DocType |
face recognition,images detect,physics-based liveness detection,linear svm classifier,authentication,feature extraction,lcd screen,false acceptance rate,single image,false rejection rate,liquid crystal displays,support vector machines,robustness,histograms,face,image resolution,image features | Computer vision,Facial recognition system,Pattern recognition,Computer science,Support vector machine,Word error rate,Robustness (computer science),Feature extraction,Artificial intelligence,Classifier (linguistics),Image resolution,Liveness | Conference |
ISSN | ISBN | Citations |
0271-4302 | 978-1-4244-5309-2 | 39 |
PageRank | References | Authors |
2.88 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiamin Bai | 1 | 184 | 10.71 |
| Tian-Tsong Ng | 2 | 694 | 43.29 |
| Xinting Gao | 3 | 119 | 10.60 |
| Yun-Qing Shi | 4 | 772 | 42.97 |