Facial Fraud Discrimination Using Detection and Classification
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.
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