Thermal super-pixels for bimodal stress recognition

Irani, Ramin; Nasrollahi, Kamal; Dhall, Abhinav; Moeslund, Thomas B.; Gedeon, Tom

doi:10.1109/ipta.2016.7821002

Cited by 17 publications

(9 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show the average accuracy of classification by this model is 87%. Proposed model performs better than similar accuracy produced in [117].…”

Section: Stress Classification Based On Facial Skin Temperature Modelmentioning

confidence: 68%

Human Stress Recognition from Facial Thermal-Based Signature: A Literature Survey

Arasu¹,

Mohamed²,

Ruhaiyem³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

Stress is a normal reaction of the human organism which triggered in situations that require a certain level of activation. This reaction has both positive and negative effects on everyone's life. Therefore, stress management is of vital importance in maintaining the psychological balance of a person. Thermal-based imaging technique is becoming popular among researchers due to its non-contact conductive nature. Moreover, thermal-based imaging has shown promising results in detecting stress in a non-contact and non-invasive manner. Compared to other non-contact stress detection methods such as pupil dilation, keystroke behavior, social media interaction and voice modulation, thermal-based imaging provides better features with clear boundaries and requires no heavy methodology. This paper presented a brief review of previous work on thermal imaging related stress detection in humans. This paper also presented the stages of stress detection based on thermal face signatures such as dataset type, thermal image face detection, feature descriptors and classification performance comparisons are presented. This paper can help future researchers to understand stress detection based on thermal imaging by presenting the popular methods previous researchers use for stress detection based on thermal images.

show abstract

“…The results show the average accuracy of classification by this model is 87%. Proposed model performs better than similar accuracy produced in [117].…”

Section: Stress Classification Based On Facial Skin Temperature Modelmentioning

confidence: 68%

Human Stress Recognition from Facial Thermal-Based Signature: A Literature Survey

Arasu¹,

Mohamed²,

Ruhaiyem³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…In the method using NIR images, stress recognition was performed using an SVM after extracting scale-invariant feature transform (SIFT) descriptors around facial landmarks. In other studies, stress was recognized by fusing RGB and thermal images [28,39,40]. In these methods, stress was recognized using the features extracted from super-pixels and local binary patterns on the three orthogonal plane (LBP-TOP) descriptor.…”

Section: Facial-image-based Stress Recognition Methodsmentioning

confidence: 99%

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

Jeon

Bae

Lee

et al. 2021

Sensors

View full text Add to dashboard Cite

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

show abstract

“…Furthermore, [ 35 ] further extended the work by representing a thermal image as a group of super-pixels, and extracting the features from thermal super-pixels rather than from pixels directly as done in [ 34 ]. According to [ 36 ], Super-pixel (a group of adjacent pixels which have similar characteristics and special information) representation has been used for face recognition.…”

Section: Related Workmentioning

confidence: 99%

Video-Based Stress Detection through Deep Learning

Zhang

Feng

et al. 2020

Sensors

View full text Add to dashboard Cite

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%.

show abstract

Thermal super-pixels for bimodal stress recognition

Cited by 17 publications

References 30 publications

Human Stress Recognition from Facial Thermal-Based Signature: A Literature Survey

Human Stress Recognition from Facial Thermal-Based Signature: A Literature Survey

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information

Video-Based Stress Detection through Deep Learning

Contact Info

Product

Resources

About