A Companion Robot With Facial Expressions

A Companion Robot with Facial Expressions and Face Recognition Hsuan-Kuan Huang, Hung-Hsiu Yu Mechanical Systems Research Laboratories (MSL), Industrial Technology Research Institute (ITRI) Hsinchu, Taiwan, R.O.C. [email protected], [email protected] Yea-Shuan Huang Computer Science & Information Engineering Chung Hua University Hsinchu, Taiwan, R.O.C. [email protected] Abstract —The purpose of this paper is to develop a companion robot, which can display facial expressions and recognize human faces. With the 12 degrees of freedom, the robot can generate various facial expressions. In addition, a face-recognition method is proposed based on combining two complementary matching algorithms (a single-image matching algorithm and a sequential- image matching algorithm). We have conducted several experiments to test the developed robot. Experiments showed that facial expressions generated by the robot can be identified well. In addition, the accuracy of the face-recognition is higher than 90%. The developed robot shows great potential to be applied for social interaction with the humans. Keywords-companion robot; facial expression; face recognition I. I NTRODUCTION To serve as a good companion and interact socially with humans, a robot must be able to do more than simply gather information about its surroundings. It must also be able to express its state, so that humans will believe that the robot has beliefs, desires and intentions of its own [1]. In addition, as more than 60% of human communication is conducted non- verbally by facial expressions and gestures [2], it is obvious that one important research topic for human-robot interaction (HRI) is to make a robot that can interact with the humans by generating different kinds of facial expressions. To achieve this objective, there have been several robot faces or robot heads designed and built so far. Researchers at the Science University of Tokyo have developed a human-like robot face that has silicon skin, teeth, hair and a large number of control points [3]. The robot can express the six basic facial expressions (happiness, anger, sadness, surprise, disgust, and fear) defined by Ekman and Friesen [4]. It can also recognize human facial expressions using CCD cameras and reciprocate the same expression back. Breazeal at MIT has developed an expressive anthropomorphic robot called Kismet [1]. It can engage with people in expressive face-to-face interaction. It has a total 15 degrees of freedom (DOF) to move its facial features (e.g. eyelids, eyebrows, lips, and ears) to generate various facial expressions. After developing Kism et, Breazeal’s group at MIT further developed the robot, Leonardo [5]. The robot has 61 DOF, where 32 are located in the face. This has enable Leonardo to show near-human facial expressions. It is considered by many researchers as the most expressive and complex face robot to date. The researchers at Takanish’s laboratory developed a robot called WE-4 (Waseda Eye No. 4). WE-4 simulated four of the five human senses (the visual, auditory, cutaneous and olfactory senses) and realized facial expressions using eyebrow, eyelids, facial color, lips and voice [6]. The robot is able to interact bilaterally with humans. Sosnowski et al. [7] developed a flexible low-cost expression-display, named EDDIE, with 23 DOFs. Actuators are assigned to particular actions of the facial action coding system (FACS). The display of six basic facial expressions is evaluated in a user-study and the results showed that the generated expressions by EDDIE can be recognized well. Berns and Hirth [2] constructed a humanoid robot head, ROMAN, for facial expressions. They adopted a behavior- based control architecture to realize six basic facial expressions. Every expression is related to one behavior-node. The control of these six behaviors is done similar to the Kismet [1]. Another important area of research on HRI is human face recognition. A lot of research efforts have been devoted to this field, and many face recognition approaches based on a variety of machine learning theorem have been developed already. For example, subspace methods such as Principle Components Analysis PCA [8,9] and Linear Discrimination Analysis (LDA) [10,11] are commonly used which project high dimensional features to low dimensional features and not only faster but also better recognition can be achieved. It is well known that face images are easy to change in color and in shape when there is variation of environment lighting, facial expressions and poses. Therefore, it is apt to be unreliable if recognition is performed based on just a single input image. To obtain a more robust recognition, Yamaguchi proposed the Mutual Subspace Method (MSM) [12] and the Constrained Mutual Subspace Method (CMSM) [13-15], and both methods perform recognition by using multiple sequential images. In MSM, similarity is defined to be the minimum angle between the input subspace and a reference subspace. In CMSM, it further projects each individual subspace including the input and the reference subspaces onto a constrained subspace. According to the projection on the same constrained subspace, it can obtain the features which have good 216 978-1-4244-5046-6/10/$26.00 c 2010 IEEE

A Companion Robot With Facial Expressions

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