Facial Expression Generation of an Android Robot based on probabilistic Model

Hyung, Hyun-Jun; Lee, Dong‐Wook; Yoon, Han Ul; Choi, Dongwoon; Lee, Duk-Yeon; Hur, Manhong

doi:10.1109/roman.2018.8525574

Cited by 9 publications

(2 citation statements)

References 3 publications

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“…The waist and neck have three degrees of freedom to allow EveR to rotate its waist and neck like humans. Finally, both arms can express a range of gestures by using 12 motors [13,14].…”

Section: Ever Androidsmentioning

confidence: 99%

“…Behavioral expression technology is currently aimed at enabling androids to exactly imitate human facial expressions and gestures [13][14][15]. The behavioral expressions of robots can largely be classified as facial expressions, gestures, lip syncing and eye contact [16,17], which also vary depending on if there are one or multiple interaction targets.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing Android Facial Expressions Using Genetic Algorithms

et al. 2019

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Because the internal structure, degree of freedom, skin control position and range of the android face are different, it is very difficult to generate facial expressions by applying existing facial expression generation methods. In addition, facial expressions differ among robots because they are designed subjectively. To address these problems, we developed a system that can automatically generate robot facial expressions by combining an android, a recognizer capable of classifying facial expressions and a genetic algorithm. We have developed two types (older men and young women) of android face robots that can simulate human skin movements. We selected 16 control positions to generate the facial expressions of these robots. The expressions were generated by combining the displacements of 16 motors. A chromosome comprising 16 genes (motor displacements) was generated by applying real-coded genetic algorithms; subsequently, it was used to generate robot facial expressions. To determine the fitness of the generated facial expressions, expression intensity was evaluated through a facial expression recognizer. The proposed system was used to generate six facial expressions (angry, disgust, fear, happy, sad, surprised); the results confirmed that they were more appropriate than manually generated facial expressions.

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“…The waist and neck have three degrees of freedom to allow EveR to rotate its waist and neck like humans. Finally, both arms can express a range of gestures by using 12 motors [13,14].…”

Section: Ever Androidsmentioning

confidence: 99%