计算机唇语识别程序处于研发阶段

A computer is being taught to interpret human emotions based on lip pattern, according to research published in the International Journal of Artificial Intelligence and Soft Computing¹. The system could improve the way we interact with computers and perhaps allow disabled people to use computer-based communications devices, such as voice synthesizers（语音合成器）, more effectively and more efficiently². Karthigayan Muthukaruppanof Manipal International University in Selangor, Malaysia, and co-workers have developed a system using a genetic³ algorithm that gets better and better with each iteration（迭代，反复） to match irregular ellipse fitting equations to the shape of the human mouth displaying different emotions. They have used photos of individuals from South-East Asia and Japan to train a computer to recognize the six commonly accepted human emotions -- happiness, sadness, fear, angry, disgust, surprise -- and a neutral expression. The upper and lower lip is each analyzed⁴ as two separate ellipses⁵ by the algorithm.   "In recent years, there has been a growing interest in improving all aspects of interaction between humans and computers especially in the area of human emotion recognition by observing facial expression," the team explains. Earlier researchers have developed an understanding that allows emotion to be recreated by manipulating a representation of the human face on a computer screen. Such research is currently informing the development of more realistic animated⁶ actors and even the behavior of robots. However, the inverse⁷（相反的） process in which a computer recognizes the emotion behind a real human face is still a difficult problem to tackle.   It is well known that many deeper emotions are betrayed by more than movements of the mouth. A genuine smile for instance involves flexing⁸ of muscles around the eyes and eyebrow⁹ movements are almost universally essential to the subconscious¹⁰ interpretation¹¹ of a person's feelings. However, the lips remain a crucial part of the outward（外面的） expression of emotion. The team's algorithm can successfully classify the seven emotions and a neutral expression described.   The researchers suggest that initial applications of such an emotion detector¹² might be helping¹³ disabled patients lacking speech to interact more effectively with computer-based communication devices, for instance.