Monte Carlo Based Importance Estimation of Localized Feature Descriptors for the Recognition of Facial Expressions

Aim of the research The literature on emotion recognition from facial expressions shows significant differences in recognition ability depending on the proposed stimulus. Indeed, affective information is not distributed uniformly in the face and recent studies showed the importance of the mouth and the eye regions for a correct recognition. However, previous studies used mainly facial expressions presented frontally and studies which used facial expressions in profile view used a between-subjects design or children faces as stimuli. The present research aims to investigate differences in emotion recognition between faces presented in frontal and in profile views by using a within subjects experimental design. Method The sample comprised 132 Italian university students (88 female, Mage = 24.27 years, SD = 5.89). Face stimuli displayed both frontally and in profile were selected from the KDEF set. Two emotion-specific recognition accuracy scores, viz., frontal and in profile, were computed from the average of correct responses for each emotional expression. In addition, viewing times and response times (RT) were registered. Results Frontally presented facial expressions of fear, anger, and sadness were significantly better recognized than facial expressions of the same emotions in profile while no differences were found in the recognition of the other emotions. Longer viewing times were also found when faces expressing fear and anger were presented in profile. In the present study, an impairment in recognition accuracy was observed only for those emotions which rely mostly on the eye regions.

Download Full-text

Toward Smart Internet of Things (IoT) Devices: Exploring the Regions of Interest for Recognition of Facial Expressions using Eye-gaze Tracking

2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) ◽

10.1109/ccece47787.2020.9255696 ◽

2020 ◽

Author(s):

Abdallah S. Abdallah ◽

Lisa J. Elliott ◽

Daniel Donley

Keyword(s):

Internet Of Things ◽

Facial Expressions ◽

Eye Gaze ◽

Regions Of Interest ◽

Gaze Tracking ◽

Recognition Of Facial Expressions ◽

Iot Devices

Download Full-text

The Effects of Coaching Subjects in the Recognition of Facial Expressions

The Journal of General Psychology ◽

10.1080/00221309.1932.9918451 ◽

1932 ◽

Vol 7 (1) ◽

pp. 163-178 ◽

Cited By ~ 10

Author(s):

Arthur Jenness

Keyword(s):

Facial Expressions ◽

Recognition Of Facial Expressions

Download Full-text

SonicFace

Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies ◽

10.1145/3494988 ◽

2021 ◽

Vol 5 (4) ◽

pp. 1-33

Author(s):

Yang Gao ◽

Yincheng Jin ◽

Seokmin Choi ◽

Jiyang Li ◽

Junjie Pan ◽

...

Keyword(s):

Facial Expressions ◽

Microphone Array ◽

Wearable Sensors ◽

Real Life ◽

Privacy Concerns ◽

Fine Grained ◽

Recognition Of Facial Expressions ◽

Ubiquitous Sensing ◽

Contact Free

Accurate recognition of facial expressions and emotional gestures is promising to understand the audience's feedback and engagement on the entertainment content. Existing methods are primarily based on various cameras or wearable sensors, which either raise privacy concerns or demand extra devices. To this aim, we propose a novel ubiquitous sensing system based on the commodity microphone array --- SonicFace, which provides an accessible, unobtrusive, contact-free, and privacy-preserving solution to monitor the user's emotional expressions continuously without playing hearable sound. SonicFace utilizes a pair of speaker and microphone array to recognize various fine-grained facial expressions and emotional hand gestures by emitted ultrasound and received echoes. Based on a set of experimental evaluations, the accuracy of recognizing 6 common facial expressions and 4 emotional gestures can reach around 80%. Besides, the extensive system evaluations with distinct configurations and an extended real-life case study have demonstrated the robustness and generalizability of the proposed SonicFace system.

Download Full-text