# Optimizing Android Facial Expressions Using Genetic Algorithms
## 1 Introduction
The descriptors they proposed can be Unlike a virtual face, which directly controls virtual skin, the robot must control a motor to move real skin. Because the correlation of internal wiring and the intensity of skin differ among robots, it is very difficult to predict the motion of the skin and control the motor. In other words, the robot is deformed as the motor moves. It is difficult to use the 3D facial analysis data because the motion of the motor due to the deformation of the skin depends upon the characteristics of the robot. Thus, the facial expressions of an android are important for delivering information from the robot to humans. Many studies have been conducted on the generation of natural robot facial expressions. [PERSON], an android developed by Hanson Robotics, was trained to generate facial expressions through the inverse nonlinear mapping of the human facial feature space to actuator space [28]. [PERSON], an android developed by ATR, achieved behavioral expressions that could be utilized in polite situations. The developers generated natural smiling behavioral expressions by considering the android's facial expressions, as well as movements of the eyes, neck and waist [29]. Moreover, [PERSON] proposed a mechanism that can generate various facial expressions by using the minimum number of actuators in consideration of production cost [30]. [PERSON] developed a facial expression generation system capable of imitating human muscle structures based on the EMG signals used for facial expressions [31]. Furthermore, [PERSON] et al. created an RNN-based forward kinematics model by measuring facial geometric deformation features from human facial expression sequence data and then generated facial expressions by producing an IK solver and applying it to an android [32]. As android faces have different presentational forms, internal mechanism structures, numbers of motors and muscle expression mechanisms, their facial expression generation methods also differ, making it difficult to mathematically define these generation methods. Most android developers generate facial expressions by controlling the motors and presenting subjective interpretations of facial expression characteristics. Thus, the results will differ depending on the skills of the developer and the same facial expression may be interpreted differently by different people. Therefore, an index is required to objectively evaluate the facial expressions generated by an android. To address these problems, this study proposes a system for androids that can automatically generate the facial expressions of an android based on a genetic algorithm. Because the face of the android resembles a human being, it is possible to utilize a human facial expression recognizer directly. This system comprises a facial expression recognizer, an android capable of generating facial expressions and a genetic algorithm. The system features a number of prerequisites. First, the facial expression recognizer should be able to identify the android's facial expressions as effectively as it can recognize those of a human. Second, the android must be able to generate facial expressions by using a combination of motor displacements inside the mechanism. Finally, to utilize the genetic algorithm, the motor displacements must be able to be expressed as genes to evaluate the fitness of the facial expressions using recognizers. By doing so, the android's facial expressions can evolve through processes such as selection, crossover and mutation, thereby generating natural facial expressions. The rest of this paper is structured as follows. In Section 2, we describe the EveR series of androids, to which the proposed system is applied. Section 3 describes how to encode facial expressions into genes and how to measure the fitness of the generated facial expressions, as well as our complete optimization system that comprises the android, the facial expression recognizer and the genetic algorithm for facial expression optimization.