Eye correction using correlation information
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Face detection with the modified census transform
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Face occlusion detection for automated teller machine surveillance
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Robust real-time face detection using face certainty map
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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This paper proposes facial fraud discrimination using facial feature detection and classification based on the AdaBoost and a neural network. The proposed method detects the face, the two eyes, and the mouth by the AdaBoost detector. To classify detection results as either normal or abnormal eyes and mouths, we use a neural network. Using these results, we calculate the fraction of face images that contain normal eyes and mouths. These fractions are used for facial fraud detection by setting a threshold based on the cumulative density function of the Binomial distribution. The FRR and FAR of eye discrimination of our algorithm are 0.0486 and 0.0152, respectively. The FRR and FAR of mouth discrimination of our algorithm are 0.0702 and 0.0299, respectively.