Abstract
The human mood has a temporary effect on the face shape due to the movement of its muscles. Happiness, sadness, fear, anger, and other emotional conditions may affect the face biometric system’s reliability. Most of the current studies on facial expressions are concerned about the accuracy of classifying the subjects based on their expressions. This study investigated the effect of facial expressions on the reliability of a face biometric system to find out which facial expression puts the biometric system at greater risk. Moreover, it identified a set of facial features that have the lowest facial deformation caused by facial expressions to be generalized during the recognition process, regardless of which facial expression is presented. In order to achieve the goal of this study, an analysis of 22 facial features between the normal face and six universal facial expressions is obtained. The results show that the face biometric systems are affected by facial expressions where the disgust expression achieved the most dissimilar score, while the sad expression achieved the lowest dissimilar score. Additionally, the study identified the five and top ten facial features that have the lowest facial deformations on the face shape in all facial expressions. Besides that, the relativity score showed less variances between the sample using the top facial features. The obtained results of this study minimized the false rejection rate in the face biometric system and subsequently the ability to raise the system’s acceptance threshold to maximize the intrusion detection rate without affecting the user convenience.
Highlights
Authentication is the mainline in the war to verify a user’s identity and reject an illegitimate user from accessing their resources
(4) What is the impact of each Facial expressions (FE) on the similarity score? (5) Which facial features have the lowest facial deformation that can be generalized during the recognition and cannot be affected significantly by the expressed emotion? (6) What is the false rejection rate (FRR) performance under the influence of FE?
The following analysis shows how, and which set of measured face’s features have the best and worst score in terms of the relativity shift score for the happy expression compared with neutral mode
Summary
Authentication is the mainline in the war to verify a user’s identity and reject an illegitimate user from accessing their resources. Three types of authentication can distinguish any person among the population; one approach concerns the user’s knowledge—such as a password, the second approach concerns what the user has—such as a national ID card, while the third approach is to define the user themselves using their humanistic traits—“biometrics”. The face recognition process is more complicated than with other biometrics, such as fingerprint and iris identification, since the human face can be viewed from various angles with different poses. Face biometrics are more difficult than other biometrics (such as fingerprint and iris) due to the fact that the human face can be viewed from various angles with different expressions. A facial biometric system’s reliability can be affected by the subject’s facial expressions; happiness and sadness and other facial emotions may lead to varying levels of facial identification accuracy and as a consequence have an effect on the system’s reliability [10]
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