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The secret of facial expressions: Are our emotions really so easy to identify?
Advances in modern technology are enabling us to increasingly understand and decode human emotions. Facial expressions, as a form of nonverbal communication, have long been considered central to emotional expression. However, can we really correctly understand other people's inner feelings based solely on facial expressions?
Affective computing is an interdisciplinary field that studies and develops systems and devices that can recognize, interpret, process, and simulate human emotions.
The roots of affective computing can be traced back to early philosophical discussions, especially in Rosalind Picard’s 1995 paper “Affective Computing,” in which she proposed a vision of giving machines emotional intelligence. Allowing them to understand and simulate human emotions, and even show empathy.
In various fields of affective computing, a key link is to detect and identify emotional information. This process usually starts with passive sensors that collect data on the user's physiological state or behavior. The data are similar to the cues that humans use to sense the emotions of others, such as facial expressions, body postures and voice characteristics.
Affective computing technology can identify the user's emotional state by analyzing physiological data.
Of course, facial expression recognition relies not only on obvious expressions, but also on more subtle facial changes, such as the wrinkling of the brow or the raising of the corners of the mouth. This can be achieved through machine learning techniques, which can extract meaningful patterns from data. The goal is to generate emotion labels that match what a human would express in the same situation, whether it's "confused" or "happy."
In terms of technology, emotion simulation has also become a hot topic. Many designers of chatbots and virtual humans try to make their creations show emotions. Marvin Minsky, for example, has pointed out that emotions are not fundamentally different from so-called “thinking” processes.
Another important direction for expressing emotions in machines is to enhance the ability of human-computer interaction.
In the current technological context, many emotion recognition systems use various types of machine learning to handle the continuous or categorical nature of emotions. These systems can identify emotions based on changes in voice, and studies have shown that their accuracy is higher than that of humans. French, intonation, and speaking speed are all considered effective indicators for emotion recognition. Research reports indicate that the accuracy of speech-based emotion recognition can reach up to 80%.
However, systems that rely on standard datasets for training also face challenges. Most existing data is obtained from actors’ performances, and these “wiggly” emotional expressions may not accurately reflect the emotional state in daily life.
Natural sentiment data is difficult to obtain, but it is very valuable in practical applications.
In the process of emotion recognition, the establishment of facial expression database is also crucial. These databases contain images and videos of various emotions, which researchers can use to improve recognition systems. However, traditional databases are often composed of participants' active expressions of emotion, which may not have the same effect as spontaneous emotional expressions.
In addition, emotion recognition can also be performed through body movements and physiological monitoring. This approach can comprehensively consider multiple signals to more accurately analyze emotional states. Physiological signals such as heart rate and galvanic skin response can provide additional insights.
In general, the development of facial expression recognition and emotional computing still faces many challenges and difficulties. Will we ever get to the point where machines can fully understand and adapt to human emotions? Does this affect how we think about relationships?
