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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facial expressions expression intensity realcoded genetic algorithms android a recognizer fitness freedom skin control structure degree

Hyun-Jun Hyung,Han Ul Yoon,Dongwoon Choi,Duk-Yeon Lee,Dong-Wook Lee,.Optimizing Android Facial Expressions Using Genetic Algorithms. 9 (16),.

Hyun-Jun Hyung,Han Ul Yoon,Dongwoon Choi,Duk-Yeon Lee,Dong-Wook Lee,"Optimizing Android Facial Expressions Using Genetic Algorithms" 9

Hyun-Jun Hyung,Han Ul Yoon,Dongwoon Choi,Duk-Yeon Lee,Dong-Wook Lee,"Optimizing Android Facial Expressions Using Genetic Algorithms"