Tied gender condition for facial expression recognition with deep random forest

Zhong, Liangji; Liao, Hui; Xu, Bin; Lu, Shejie; Wang, Jianfeng

doi:10.1117/1.jei.29.2.023019

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al [25] utilized the facial action unit to recognize the expressions. Liang ji et al [26] proposed deep learning enhanced gender conditional random forest for expressions in an uncontrolled environment to address the gender influence. Jeong et al [27] proposed deep joint spatiotemporal features for facial expression recognition based on the deep appearance and geometric neural networks.…”

Section: Facial Expression Recognition Applicationsmentioning

confidence: 99%

“…For instance, in [15,16] investigated the utilization of the selected features and landmarks for face recognition purposes only. Although the accuracy was the highest when both slopes and distances were used in [12], this study will use distances only as it analyzes which muscles and facial features are affected by FE, not for recognition purposes [21][22][23][24][25][26][27][28][29][30][31][32][33], evaluated the performance of FE classifications. While utilizing the periocular as a biometric trait in [33] has its failures when the face presents posture changes, occlusions, closed eyes, and other changes, in the FB, the recognition process can use other features than the one that exposes failure.…”

Section: The Effect Of Facial Expression On Face Biometric Reliabilitymentioning

confidence: 99%

See 1 more Smart Citation

The Effects of Facial Expressions on Face Biometric System’s Reliability

Alrubaish

Zagrouba

2020

Information

View full text Add to dashboard Cite

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.

show abstract

Section: Facial Expression Recognition Applicationsmentioning

confidence: 99%