Emotion recognition is a system that tries to infer a person’s emotional state, stress level, or intent from observed signals such as facial expression, voice, text, or posture. It does not measure emotion directly; it applies statistical models to patterns in data and produces a probability-based guess. These systems are often built with multimodal analysis, combining audio, visual, and language inputs to raise confidence.
In cyber security, emotion recognition matters because it can be used for monitoring, profiling, and persuasion. Defenders may use it in customer support analytics or training, but attackers can also exploit it in social engineering, tailoring phishing, deepfake voice calls, or chatbot interactions to a target’s mood. The main risks are false inference, hidden surveillance, and abuse of sensitive behavioral data. Strong controls include consent, purpose limitation, access control, retention limits, and careful review of whether the system should be used at all.