Using Score Normalization to Solve the Score Variation Problem in Face Authentication

Abstract

AbstractThis paper investigates the score normalization technique for enhancing the performance of face authentication. We firstly discuss the thresholding approach for face authentication and put forward the “score variation” problem. Then, two possible solutions, Subject Specific Threshold (SST) and Score Normalization (SN), are discussed. But SST is obviously impractical to many face authentication applications in which only a single example face image is available for each subject. Fortunately, we have theoretically shown that, in such cases, score normalization technique may approximately approach the SST by using a uniform threshold. Experiments on both the FERET and CAS-PEAL face database have shown the effectiveness of SN for different face authentication methods including Correlation, Eigenface, and Fisherface.KeywordsScore NormalizationFalse Alarm RateSimilarity ScoreFace DatabaseSpeaker VerificationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.