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Evaluation of Fear Using Nonintrusive Measurement of Multimodal Sensors.

Choi JS, Bang JW, Heo H, Park KR - Sensors (Basel) (2015)

Bottom Line: Further, the latter causes inconvenience to the user due to the sensors attached to the body.Among various emotions, the accurate evaluation of fear is crucial in many applications, such as criminal psychology, intelligent surveillance systems and the objective evaluation of horror movies.Therefore, we propose a new method for evaluating fear based on nonintrusive measurements obtained using multiple sensors.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. jjongssuk@dgu.edu.

ABSTRACT
Most previous research into emotion recognition used either a single modality or multiple modalities of physiological signal. However, the former method allows for limited enhancement of accuracy, and the latter has the disadvantages that its performance can be affected by head or body movements. Further, the latter causes inconvenience to the user due to the sensors attached to the body. Among various emotions, the accurate evaluation of fear is crucial in many applications, such as criminal psychology, intelligent surveillance systems and the objective evaluation of horror movies. Therefore, we propose a new method for evaluating fear based on nonintrusive measurements obtained using multiple sensors. Experimental results based on the t-test, the effect size and the sum of all of the correlation values with other modalities showed that facial temperature and subjective evaluation are more reliable than electroencephalogram (EEG) and eye blinking rate for the evaluation of fear.

No MeSH data available.


Related in: MedlinePlus

Example of detecting pupil regions using sub-block-based template matching.
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sensors-15-17507-f009: Example of detecting pupil regions using sub-block-based template matching.

Mentions: To measure the change of eye blinking rate according to fear, our system used a high-performance (high-speed mega-pixel) camera [20]. We refer to the existing research [27] into detecting pupils and measuring eye blinking rate. Firstly, the corneal specular reflection (SR) is located using image binarization. Based on the detected region of the corneal SR, the ROI for pupil detection is defined as shown in the red boxes of Figure 9. Within these two ROIs, pupil areas are located by sub-block-based template matching. A 3 × 3 mask, including nine sub-blocks (M0–M8) is used for the sub-block-based template-matching algorithm, as shown in Figure 9. Using the sub-block-based template-matching, the pupil region is approximately located. Then, the accurate locations of the boundary and center of the pupil are determined using image binarization, boundary detection and an ellipse-fitting method. If the ellipse-fitting method successfully detects the boundary and center of the pupil, our system determines that the user’s eyes are open. If the detection fails, the user’s eyes are determined to be closed, as shown in Figure 10. Finally, our system measures the number of open to closed eyes for a duration of 1 min, which is the eye blinking rate. If the subject keeps his or her eyes closed, the number of open to closed eyes for a duration of 1 min is maintained, and the consequent eye blinking rate becomes smaller than that of normal eye blinking cases. Because these kinds of data are not the correct ones for measuring the fear of the subject, we removed these data in our experiments.


Evaluation of Fear Using Nonintrusive Measurement of Multimodal Sensors.

Choi JS, Bang JW, Heo H, Park KR - Sensors (Basel) (2015)

Example of detecting pupil regions using sub-block-based template matching.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541947&req=5

sensors-15-17507-f009: Example of detecting pupil regions using sub-block-based template matching.
Mentions: To measure the change of eye blinking rate according to fear, our system used a high-performance (high-speed mega-pixel) camera [20]. We refer to the existing research [27] into detecting pupils and measuring eye blinking rate. Firstly, the corneal specular reflection (SR) is located using image binarization. Based on the detected region of the corneal SR, the ROI for pupil detection is defined as shown in the red boxes of Figure 9. Within these two ROIs, pupil areas are located by sub-block-based template matching. A 3 × 3 mask, including nine sub-blocks (M0–M8) is used for the sub-block-based template-matching algorithm, as shown in Figure 9. Using the sub-block-based template-matching, the pupil region is approximately located. Then, the accurate locations of the boundary and center of the pupil are determined using image binarization, boundary detection and an ellipse-fitting method. If the ellipse-fitting method successfully detects the boundary and center of the pupil, our system determines that the user’s eyes are open. If the detection fails, the user’s eyes are determined to be closed, as shown in Figure 10. Finally, our system measures the number of open to closed eyes for a duration of 1 min, which is the eye blinking rate. If the subject keeps his or her eyes closed, the number of open to closed eyes for a duration of 1 min is maintained, and the consequent eye blinking rate becomes smaller than that of normal eye blinking cases. Because these kinds of data are not the correct ones for measuring the fear of the subject, we removed these data in our experiments.

Bottom Line: Further, the latter causes inconvenience to the user due to the sensors attached to the body.Among various emotions, the accurate evaluation of fear is crucial in many applications, such as criminal psychology, intelligent surveillance systems and the objective evaluation of horror movies.Therefore, we propose a new method for evaluating fear based on nonintrusive measurements obtained using multiple sensors.

View Article: PubMed Central - PubMed

Affiliation: Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Korea. jjongssuk@dgu.edu.

ABSTRACT
Most previous research into emotion recognition used either a single modality or multiple modalities of physiological signal. However, the former method allows for limited enhancement of accuracy, and the latter has the disadvantages that its performance can be affected by head or body movements. Further, the latter causes inconvenience to the user due to the sensors attached to the body. Among various emotions, the accurate evaluation of fear is crucial in many applications, such as criminal psychology, intelligent surveillance systems and the objective evaluation of horror movies. Therefore, we propose a new method for evaluating fear based on nonintrusive measurements obtained using multiple sensors. Experimental results based on the t-test, the effect size and the sum of all of the correlation values with other modalities showed that facial temperature and subjective evaluation are more reliable than electroencephalogram (EEG) and eye blinking rate for the evaluation of fear.

No MeSH data available.


Related in: MedlinePlus