Emotion AI & Facial Emotion Recognition – FAQ

Emotion AI, also known as affective computing, refers to the field of technology that focuses on recognizing, interpreting, and responding to human emotions. It combines techniques from computer vision, natural language processing, and machine learning to analyze facial expressions, vocal intonations, body language, and other cues to infer human emotions and provide contextually appropriate responses.

Facial Emotion Recognition (FER) refers to the process by which a system or software identifies and classifies human emotions based on facial expressions. By examining various facial features such as eyebrow movement, eye dilation, mouth shape, and overall facial muscle activity, the technology aims to identify and classify emotions such as happiness, sadness, anger, surprise, fear, and disgust. The technology used by MorphCast combines computer vision and convolutional deep neural network algorithms to analyze images or videos of people’s faces and identify patterns that correspond to specific emotions.

Dr. Ekman identified the six basic emotions as anger, surprise, disgust, enjoyment, fear, and sadness. The Ekman model also takes into consideration a seventh core emotion, the neutral expression.

The Circumplex Model of Affect, developed by James Russell, offers a nuanced perspective on human emotions by mapping them within a two-dimensional space defined by arousal (level of alertness) and valence (the positivity or negativity of emotions). Unlike the basic six emotions model which categorizes emotions as discrete entities, Russell’s model allows for a more fluid and comprehensive representation of emotional states. By plotting emotions on the axes of arousal and valence, the model demonstrates how emotions interrelate and vary in intensity and pleasantness. This approach provides a more dynamic understanding of emotions, recognizing the complexity and spectrum of human feelings beyond the basic categories.
For more detailed insights into the Circumplex Model of Affect and its application, visit MorphCast’s guide on interpreting Emotion AI output data.

Facial Expression Recognition by MorphCast, extends beyond identifying fundamental emotions, as defined by Ekman, or affects, as characterized by Russell. It can gauge levels of attention and engagement, as well as discern attributes like the presence of glasses, earrings, specific skin features, or haircuts. With MorphCast, we offer an advanced AI engine that detects over 100 signals through facial analysis. All these capabilities are compactly bundled into a potent AI model that operates directly within a user’s device browser. See this specific documentation about data extracted and represented as well as how it works.

There’s not one single emotion that’s easiest to detect. However, many studies have shown that emotion recognition technology is more accurate for certain emotions (e.g., happiness, anger) than others (e.g., sadness, surprise).

MorphCast uses a combination of computer vision and convolutional deep neural network algorithms to analyze images or videos of people’s faces and identify patterns that correspond to specific emotions. The algorithms use deep learning techniques like convolutional neural networks (CNNs) and are trained on large datasets. Read more about MorphCast methods.

The accuracy of emotion AI facial recognition technology can be affected by several factors, including image or video quality, the algorithm used, and the quality and diversity of the training data. Despite these variables, the current emotion prediction accuracy is considered to be between 70% and 80% for almost all algorithms on the market. MorphCast’s accuracy has been evaluated in a university study that compares the accuracy of different software, detected on the six basic emotions of Paul Ekman, with the perception of the human being.

While AI can simulate and recognize emotions displayed by humans through facial expressions or text analysis, AI itself does not possess subjective emotional experiences like humans do. Emotions involve complex cognitive processes, personal experiences, and subjective consciousness, which are not currently within the capabilities of AI systems.

While AI can simulate and recognize emotions displayed by humans through facial expressions or text analysis, AI itself does not possess subjective emotional experiences like humans do. Emotions involve complex cognitive processes, personal experiences, and subjective consciousness, which are not currently within the capabilities of AI systems.
However, it’s important to note that AI technologies, such as Facial Emotion Recognition, can play a significant role in detecting and interpreting human emotions. This ability to “read” emotions can be used to create more empathetic interactions between humans and machines.

AI systems lack the biological and cognitive components necessary to experience emotions. Emotions are complex phenomena that involve personal experiences, subjective feelings, and conscious awareness, which are currently beyond the scope of AI capabilities. While AI can recognize and simulate emotions to a certain extent, it does not possess the underlying mechanisms and subjective experiences associated with human emotions.

Currently, AI systems lack the ability to become self-aware. Self-awareness involves having consciousness, introspection, and a deep understanding of one’s own existence, thoughts, and emotions. AI systems are designed to process data and perform tasks based on algorithms and predefined rules, without possessing the subjective awareness or introspective capabilities associated with self-awareness.

Emotion AI can be applied in the field of mental health to assist in identifying and monitoring individuals’ emotional states. By analyzing facial expressions, vocal cues, and other physiological indicators, emotion AI systems can help clinicians or mental health professionals assess and track emotional well-being, detect signs of distress, and provide personalized interventions or support.

Emotion AI is an evolving field that has been influenced by numerous researchers and innovators. Notable contributions have been made by pioneers such as Rosalind Picard, a professor at MIT Media Lab, who introduced the concept of affective computing and developed early emotion recognition technologies. Many researchers and organizations have since contributed to advancing emotion AI technologies, making it a multidisciplinary and collaborative field.

While AI can analyze facial expressions and other cues to infer human emotions, the concept of an AI specifically reading and understanding its owner’s emotions is not yet fully realized. Some AI systems may use emotion recognition to adapt responses or interactions based on general emotional states, but they do not possess the ability to grasp complex emotions on an individual level.