Emotion Affective Color Transfer Using Feature Based Facial Expression Recognition

Ryoo, Seung-Taek; Chang, Jae-Khun

doi:10.14257/astl.2013.39.24

Cited by 7 publications

(11 citation statements)

References 12 publications

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“…The automated recognition of emotions [1][2][3][4][5][6] through computational analysis is a challenging task in the field of computer vision. Facial expressions [7][8][9] are fundamental in this sense since they are one of the main signs of humanity's emotions.…”

Section: Introductionmentioning

confidence: 99%

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Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Silva

Pedrini

2015

J. Electron. Imaging

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Facial expressions are an important demonstration of humanity's humors and emotions. Algorithms capable of recognizing facial expressions and associating them with emotions were developed and employed to compare the expressions that different cultural groups use to show their emotions. Static pictures of predominantly occidental and oriental subjects from public datasets were used to train machine learning algorithms, whereas local binary patterns, histogram of oriented gradients (HOGs), and Gabor filters were employed to describe the facial expressions for six different basic emotions. The most consistent combination, formed by the association of HOG filter and support vector machines, was then used to classify the other cultural group: there was a strong drop in accuracy, meaning that the subtle differences of facial expressions of each culture affected the classifier performance. Finally, a classifier was trained with images from both occidental and oriental subjects and its accuracy was higher on multicultural data, evidencing the need of a multicultural training set to build an efficient classifier.

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Section: Introductionmentioning

confidence: 99%

“…Several works [2][3][4][6][7][8]10,11,14,15,31 have been developed by the scientific community to automatically recognize facial expressions. Some methods are based on the recognition of emotions, whereas others are based on the recognition of facial muscle actions or facial action units.…”

Section: Introductionmentioning

confidence: 99%

Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Silva

Pedrini

2015

J. Electron. Imaging

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“…We considered the first human as the experiment subject and the second human as the control subject. Based on the human-human study, we found the following major task situations/contexts/events and the corresponding affects, as given in Table 2 [45][46][47][48]. Table 2 shows the major events that occurred during the human-human collaboration and the ways the humans, especially the first humans, expressed the situations/events through affective expressions.…”

Section: Inspiration From a Human-human Collaborative Task: The Biomi...mentioning

confidence: 99%

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

Rahman

2024

Electronics

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The objective was to investigate the impacts of the robot’s dynamic affective expressions in task-related scenarios on human–robot collaboration (HRC) and performance in human–robot collaborative assembly tasks in flexible manufacturing. A human–robot hybrid cell was developed to facilitate a human co-worker and a robot to collaborate to assemble a few parts into a final product. The collaborative robot was a humanoid manufacturing robot with the ability to display its affective states due to changes in task scenarios on its face. The assembly task was divided into several subtasks, and based on an optimization strategy, the subtasks were optimally allocated to the human and the robot. A computational model of the robot’s affective states was derived inspired by that of humans following the biomimetic approach, and an affect-based motion planning strategy for the robot was proposed to enable the robot to adjust its motions and behaviors with task situations and communicate (inform) the situations to the human co-worker through affective expressions. The HRC and the assembly performance for the affect-based motion planning were experimentally evaluated based on a comprehensive evaluation scheme and were compared with two alternative conditions: (i) motion planning that did not display affective states, and (ii) motion planning that displayed text messages instead of displaying affective states to communicate the situations to the human co-worker. The results clearly showed that the dynamic affect-based motion planning produced significantly better HRC and assembly performance than that produced by motion planning associated with the display of no affective states or text messages. The results encouraged employing manufacturing robots with dynamic affective expressions to collaborate with humans in flexible assembly in manufacturing to improve HRC and assembly performance.

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“…To name a few exceptional studies, Hakim, Marsland, and Guesgen [35] used data on human faces, in which experts labelled the revealed emotion on them as training data for video classification. In addition, Ryoo and Chang [36] associated the color features of videos with the expressed emotion on human faces. However, relatively little attention has been paid to the facial emotions on human faces in affective content analysis of video, and the emotional differences according to the attributes of the video have been particularly understudied.…”

Section: Related Work a Affective Content Analysis Of Videomentioning

confidence: 99%

Exploration of the Characteristics of Emotion Distribution in Korean TV Series: Common Pattern and Statistical Complexity

Kim

Lee

2020

IEEE Access

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The series is one of the most popular formats of television programs, but the emotional aspects of its content have not attracted much attention from researchers. Furthermore, the cinemetric approach, in which move data are quantitively examined, has not been used to analyze affective content in television series. Therefore, this study focused on emotion distribution in Korean television series. In all, 337 episodes from 20 series were divided into shots, then keyframes were selected from the shots. Facial emotions on the keyframes were measured using an online artificial intelligence service. Emotion distributions were obtained from each episode and then were observed for common patterns. In addition, the statistical complexity of the distributions were calculated and compared by the temporal phase of the episode, channel type, genre, ratings, and season. The results show that the emotion distributions of all episodes in all series had the same form: an asymmetric U-shaped distribution biased toward zero. The statistical complexity of emotion distribution was greater in early episodes than in later episodes of a series, and it was higher in episodes with higher ratings. In addition, the complexity of emotion distribution was higher in hard genres and during the summer season. There was no significant difference between terrestrial television channels and cable/general programming channels.

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Emotion Affective Color Transfer Using Feature Based Facial Expression Recognition

Cited by 7 publications

References 12 publications

Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Effects of cultural characteristics on building an emotion classifier through facial expression analysis

Dynamic Affect-Based Motion Planning of a Humanoid Robot for Human-Robot Collaborative Assembly in Manufacturing

Exploration of the Characteristics of Emotion Distribution in Korean TV Series: Common Pattern and Statistical Complexity

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