Facial expression recognition

Manglik, P.K.; Misra, Udayan; Prashant, .; Maringanti, Hima Bindu

doi:10.1109/icsmc.2004.1400658

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…In addition, facial expressions have been analyzed [24] and classified [25][26][27] using TS imaging. Commonly, VS imaging has been used for modeling facial expressions, and associated robust facial recognition techniques have been developed [28][29][30]. However, from our understanding, the literature has not developed computational models for stress recognition using both TS and VS imaging together as yet.…”

Section: Introductionmentioning

confidence: 99%

Thermal spatio-temporal data for stress recognition

et al. 2014

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Stress is a serious concern facing our world today, motivating the development of a better objective understanding through the use of non-intrusive means for stress recognition by reducing restrictions to natural human behavior. As an initial step in computer vision-based stress detection, this paper proposes a temporal thermal spectrum (TS) and visible spectrum (VS) video database ANUStressDB -a major contribution to stress research. The database contains videos of 35 subjects watching stressed and not-stressed film clips validated by the subjects. We present the experiment and the process conducted to acquire videos of subjects' faces while they watched the films for the ANUStressDB. Further, a baseline model based on computing local binary patterns on three orthogonal planes (LBP-TOP) descriptor on VS and TS videos for stress detection is presented. A LBP-TOP-inspired descriptor was used to capture dynamic thermal patterns in histograms (HDTP) which exploited spatio-temporal characteristics in TS videos. Support vector machines were used for our stress detection model. A genetic algorithm was used to select salient facial block divisions for stress classification and to determine whether certain regions of the face of subjects showed better stress patterns. Results showed that a fusion of facial patterns from VS and TS videos produced statistically significantly better stress recognition rates than patterns from VS or TS videos used in isolation. Moreover, the genetic algorithm selection method led to statistically significantly better stress detection rates than classifiers that used all the facial block divisions. In addition, the best stress recognition rate was obtained from HDTP features fused with LBP-TOP features for TS and VS videos using a hybrid of a genetic algorithm and a support vector machine stress detection model. The model produced an accuracy of 86%.

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Section: Introductionmentioning

confidence: 99%

Thermal spatio-temporal data for stress recognition

et al. 2014

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“…Inspired by the research results that stress could be successfully detected from thermal imaging due to changes in skin temperature under stress [ 26 ], as well as the successful use of both thermal spectrum (TS) and visible spectrum (VS) imaging in modeling, analyzing, and recognizing facial expressions [ 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ] proposed a stress recognition method by fusing visual and thermal spectrums of spatio-temporal facial data. A temporal TS and VS video database ANUStressDB, containing videos of 35 subjects watching stressful and non-stressful film clips, was proposed for stress recognition.…”

Section: Related Workmentioning

confidence: 99%

Video-Based Stress Detection through Deep Learning

Zhang

Feng

et al. 2020

Sensors

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Stress has become an increasingly serious problem in the current society, threatening mankind’s well-beings. With the ubiquitous deployment of video cameras in surroundings, detecting stress based on the contact-free camera sensors becomes a cost-effective and mass-reaching way without interference of artificial traits and factors. In this study, we leverage users’ facial expressions and action motions in the video and present a two-leveled stress detection network (TSDNet). TSDNet firstly learns face- and action-level representations separately, and then fuses the results through a stream weighted integrator with local and global attention for stress identification. To evaluate the performance of TSDNet, we constructed a video dataset containing 2092 labeled video clips, and the experimental results on the built dataset show that: (1) TSDNet outperformed the hand-crafted feature engineering approaches with detection accuracy 85.42% and F1-Score 85.28%, demonstrating the feasibility and effectiveness of using deep learning to analyze one’s face and action motions; and (2) considering both facial expressions and action motions could improve detection accuracy and F1-Score of that considering only face or action method by over 7%.

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“…So to extract the eyebrow we have applied the method described in Manglik et al (2004) on the predicted eyebrow region according to the geometric model of Fig. 4.…”

Section: Right Eyebrow Extractionmentioning

confidence: 99%