• Science of Emotion and Sensibility
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• v.25 no.1
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• pp.21-28
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• 2022

People differ greatly in their capacity to persist in the face of challenges. Despite significant research, relatively little is known about cognitive factors that might be involved in perseverance. Building upon human threat-management mechanism, we predicted that perseverant people would be characterized by reduced sensitivity (i.e., longer detection latency) to threat cues. Our data from 5,898 job applicants showed that highly perseverant individuals required more time to correctly identify anger in faces, regardless of stimulus type (dynamic or static computer-morphed faces). Such individual differences were not observed in response to other facial expressions (happiness, sadness), and the effect was independent of gender, dispositional anxiety, or conscientiousness. Discussions were centered on the potential role of threat sensitivity in effortful pursuit of goals.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.112-118
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• 2009

This paper proposes a rapid detection algorithm of a salient region for music video browsing system, which can be applied to mobile device and digital video recorder (DVR). The input music video is decomposed into the music and video tracks. For the music track, the music highlight including musical chorus is detected based on structure analysis using energy-based peak position detection. Using the emotional models generated by SVM-AdaBoost learning algorithm, the music signal of the music videos is classified into one of the predefined emotional classes of the music automatically. For the video track, the face scene including the singer or actor/actress is detected based on a boosted cascade of simple features. Finally, the salient region is generated based on the alignment of boundaries of the music highlight and the visual face scene. First, the users select their favorite music videos from various music videos in the mobile devices or DVR with the information of a music video's emotion and thereafter they can browse the salient region with a length of 30-seconds using the proposed algorithm quickly. A mean opinion score (MOS) test with a database of 200 music videos is conducted to compare the detected salient region with the predefined manual part. The MOS test results show that the detected salient region using the proposed method performed much better than the predefined manual part without audiovisual processing.

• Journal of the Korea Society of Computer and Information
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• v.18 no.9
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• pp.131-138
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• 2013

In this paper, I propose and evaluate the method that classifies emotional type of characters with their emotional words. Emotional types are classified as three types such as positive, negative and neutral. They are selected by classification of emotional words that characters speak. I propose the method to extract emotional words based on WordNet, and to represent as emotional vector. WordNet is thesaurus of network structure connected by hypernym, hyponym, synonym, antonym, and so on. Emotion word is extracted by calculating its emotional distance to each emotional category. The number of emotional category is 30. Therefore, emotional vector has 30 levels. When all emotional vectors of some character are accumulated, her/his emotion of a movie can be represented as a emotional vector. Also, thirty emotional categories can be classified as three elements of positive, negative, and neutral. As a result, emotion of some character can be represented by values of three elements. The proposed method was evaluated for 12 characters of four movies. Result of evaluation showed the accuracy of 75%.

• Science of Emotion and Sensibility
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• v.14 no.4
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• pp.605-616
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• 2011

The present study examined memory accuracy for faces with positive, negative and neutral emotional expressions to test whether their emotional content can affect visual working memory (VWM) performance. Participants remembered a set of face pictures in which facial expressions of the faces were randomly assigned from pleasant, unpleasant and neutral emotional categories. Participants' task was to report presence or absence of an emotion change in the faces by comparing the remembered set against another set of test faces displayed after a short delay. The change detection accuracies of the pleasant, unpleasant and neutral face conditions were compared under two memory exposure duration of 500ms vs. 1000ms. Under the duration of 500ms, the accuracy in the pleasant condition was higher than both unpleasant and neutral conditions. However the difference disappeared when the duration was extended to 1000ms. The results indicate that a positive facial expression can improve VWM accuracy relative to the negative or positive expressions especially when there is not enough time for forming durable VWM representations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.4
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• pp.603-610
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• 2021

In this paper, we extract the user's power consumption patterns, and model the optimal consumption patterns by applying the user's environment and emotion. Based on the comparative analysis of these two patterns, we present an efficient power consumption method through changes in the user's power consumption behavior. To extract significant consumption patterns, vector standardization and binary data transformation methods are used, and learning about the ensemble's ensemble with k-means clustering is applied, and applying the support factor according to the value of k. The optimal power consumption pattern model is generated by applying forced and emotion-based control based on the learning results for ensemble aggregates with relatively low average consumption. Through experiments, we validate that it can be applied to a variety of windows through the number or size adjustment of clusters to enable forced and emotion-based control according to the user's intentions by identifying the correlation between the number of clusters and the consistency ratios.

• Journal of Korea Society of Industrial Information Systems
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• v.20 no.2
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• pp.57-63
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• 2015

Compared to other emotion recognition technology, facial expression recognition technology has the merit of non-contact, non-enforceable and convenience. In order to apply to a psychological robot, vision technology must be able to quickly and accurately extract the face region in the previous step of facial expression recognition. In this paper, we remove the background from any image using the YCbCr skin color technology, and use Haar-like Feature technology for robust face detection. We got the result of improved processing speed and robust face detection by removing the background from the input image.

• Science of Emotion and Sensibility
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• v.20 no.2
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• pp.137-148
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• 2017

The present study examined the effect of stimulus contrast and color on detection of a target embedded in streams of letters. In Experiment 1, each trial displayed four rapid serial visual presentation (RSVP) streams of letters (i.e., multi-RSVP), and each stream occupied one of four different locations. Each frame in the RSVP stream had four white distractors at the locations except one frame where a dim grey target was displayed at a location with three white distractors at the remaining locations. In the low-visibility target condition, the target's grey color was slightly darker than the background grey whereas much dimmer in the high-visibility condition. Participants were asked to report presence of a predesignated target as quickly and accurately as possible upon its detection in each trial, and their target detection turned out more accurate and quicker in the high-visibility than the low-visibility condition. In Experiment 2, the same RSVP displays and task as Experiment were used, but the grey target letters in the high-visibility condition were replaced with those of distinct chromatic colors. Participants detected target presence more accurately in the high-visibility condition, but the reaction time did not differ between the visibility conditions. The results indicate that higher stimulus contrast as well as distinct color can improve perception of a target stimulus displayed among visually-demanding background, but also suggest that stimulus contrast may play a more substantial role for such perceptual improvement.

• Science of Emotion and Sensibility
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• v.16 no.3
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• pp.341-350
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• 2013

The present study aimed to understand the sensory characteristic of change detection by comparing the experience of detecting a salient visual change against the experience of detecting a sensory conflict evoked by a binocular mismatch. In Experiment 1, we used the change detection task where 2, 4, or 6 items were short-term remembered in visual working memory and were compared with following test items. The half of change-present trials were manipulated to elicit a binocular rivalry on the test item with the change by way of monocular inputs across the eyes. The results showed that change detection accuracy without the rivalry manipulation declined evidently as the display setsize increased whereas no such setsize effect was observed with the rivalry manipulation. Experiment 2 tested search efficiency for the search array where the target was designated as an item with the rivalry manipulation, and found the search was very efficient regardless of the rivalry manipulation. The results of Experiment 1 and 2 showed that when the given memory load varies, the experience of detecting a salient visual change become similar to the experience of detecting a sensory conflict by a binocular rivalry.

• Science of Emotion and Sensibility
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• v.15 no.1
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• pp.37-46
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• 2012

We tested the effects of endogenous attention and reorienting on the performance of detection task. In the classic detection paradigm of Posner and Cohen (1980), performance on target detection is measured, where target appears either on the same or difference spatial location of cue stimulus after brief period of SOA (stimulus onset asynchrony). In this study, we induced exogenous attention by manipulating predictability of cue for target, and also induced reorientation by inserting additional (reorienting) cue between initial cue and target. Experiment 1 had three conditions of reorienting speed: Early, middle, and late. Facilitation and IOR (inhibition of return) occurred in different forms depending on SOA and reorienting speed, but we were not able to discover interpretable pattern in the results. However, reanalysis of early reorienting condition revealed that facilitation and IOR occurred in a crossed manner where short SOA found facilitation and long SOA did IOR, the typical results of simple detection task. Experiment 2 collected additional data to replicate the results in early reorienting condition of experiment 1. The results obtained that facilitation occurred with short SOA and IOR with long SOA. These results contrast with those of Wright and Richard (2000) where they reported elimination of IOR when cue had predictability of target locations. These results suggest that additional cue (here, orienting cue), which rapidly appears before extinction of IOR by prior cue, brings about double IOR. The present research demonstrates that even when attention is allocated to certain location via endogenous mechanism, rapidly repeating cues in certain location maximizes IOR that offsets the effects of endogenous attention to the same location.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.917-924
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• 2018

Recently, researches using LBP and SVM have been performed as one of the image - based methods for facial emotion recognition. LBP, introduced by Ojala et al., is widely used in the field of image recognition due to its high discrimination of objects, robustness to illumination change, and simple operation. In addition, CS(Center-Symmetric)-LBP was used as a modified form of LBP, which is widely used for face recognition. In this paper, we propose a method to detect four facial expressions such as expressionless, happiness, surprise, and anger using deep neural network. The validity of the proposed method is verified using accuracy. Based on the existing LBP feature parameters, it was confirmed that the method using the deep neural network is superior to the method using the Adaboost and SVM classifier.