Facial recognition has become one of the most accurate methods for identifying and authenticating individuals. Today, biometric AI is also being used to analyze emotional states, detect health patterns, and even predict certain behaviors.

Facial recognition is a probabilistic technology capable of automatically recognizing people by analyzing their facial features. It can locate a human face in an image or video, determine if the face in two images belongs to the same person, or search for a match within a large image database.

Although the technology emerged nearly 60 years ago, its capabilities have grown exponentially in the past decade. Improvements in artificial intelligence and deep learning networks have enabled significant advances in both static images and real-time video.

Facial recognition now serves a wide range of purposes across commercial sectors and public security, including police operations. It is widely used in border control, travel, retail, hospitality, and banking.

It is also an essential layer of security in smartphones and payment apps, and plays a growing role in enabling contactless access to buildings and enhancing space management.

However, our face is not only a means of identification. It is also becoming a source of deeply personal data—revealing information about our emotions, health, and even political preferences.

Detecting Emotions, Predicting Behaviors

Emotion recognition is a technology that analyzes human feelings through sources such as images and videos. It is part of a broader group of technologies known as affective computing—a multidisciplinary field that explores how computers can recognize and interpret emotions and moods using artificial intelligence models.

By training on large, categorized datasets, these algorithms learn to associate emotions with their external expressions. When combined with contextual factors and physiological signals such as heart rate, breathing patterns, or skin conductivity, the systems can identify complex emotions and even build personality profiles to predict behavior.

Help With Mental Health Diagnoses

The combination of AI and facial recognition is already being used in the healthcare sector to optimize medical data management and support clinicians in making more accurate diagnoses. In particular, it shows great potential for detecting signs of mental illness.

One example is MoodCapture, a smartphone app developed by a research team at Dartmouth College in the United States. Supported by the U.S. Department of Homeland Security, the app analyzes facial expressions and environmental cues to identify symptoms of depression. It connects to the phone’s camera and captures multiple images of the user, searching for clinical signs associated with depressive episodes. It can even estimate the severity of the condition.

The model is based on a dataset of more than 125,000 images from 177 participants diagnosed with severe depression. This allows the app to learn how to distinguish depressive states from non-depressive ones. According to its creators, the app currently achieves an accuracy rate of 75%.

Early Detection of Genetic and Coronary Diseases

Another app, developed by the Boston-based company FDNA, analyzes facial features to detect potential genetic disorders. The company initially used an algorithm trained on more than 17,000 images of diagnosed cases covering 216 different syndromes. Its latest version now includes over 150,000 images in its database and is capable of screening for a wide range of genetic conditions.

These algorithms can also be applied to assess the risk of coronary heart disease, as certain facial characteristics have been linked to this condition. In a study conducted across nine Chinese hospitals, patients underwent coronary artery CT scans to help train and validate a deep learning model. The model was designed to detect signs of heart disease from facial photographs. After the training and validation stages, the model achieved a diagnostic accuracy of 80%.

Facial analysis also extends to dermatology. The Legit.Health app allows users to analyze images of skin lesions, interpret them, and provide useful insights for healthcare professionals to support diagnosis.

Can Your Face Tell Who You Vote For?

If it seems surprising that facial recognition technology can accurately identify signs of depression, it is even more striking that it may also predict your political orientation. Researchers at Stanford University claim that AI can determine a person’s political leanings with a high degree of accuracy by analyzing facial features in combination with other factors.

The authors of the study created a database of facial images to test whether a facial recognition algorithm could reliably match faces to specific political orientations. According to their findings, the algorithm proved to be effective.

What If the Algorithm Gets It Wrong?

Several studies, including one from the American Psychological Association, suggest that it is impossible to accurately determine what a person is experiencing based solely on their facial expressions. The way people express emotions such as anger, disgust, fear, happiness, sadness, and surprise can vary significantly across cultures, contexts, and even between individuals within the same setting.

There are also biases embedded in many models. For instance, a study by the University of Maryland found that emotion recognition systems tend to associate Black individuals with emotions like anger more often than white individuals, even when both display the same facial expression. This kind of bias can lead to injustice and increased vulnerability, especially when these systems are used in video surveillance or employment-related processes.

Despite these concerns, emotion recognition technologies are expanding rapidly. Major companies like Amazon (Rekognition), Microsoft (Face API), Apple (which acquired the startup Emotient), and IBM are all developing their own systems. Their use in fields such as healthcare and security is expected to continue growing.