A review of current capabilities and future directions in machine-based emotion recognition.

The ability of machines to recognize emotions automatically is becoming increasingly significant across many domains where emotional understanding is essential. Such technology is applied in customer interaction, marketing, healthcare, education, the automotive industry, entertainment, and security. Providing real-time insights into human affective states improves user engagement and enables systems to respond more intelligently. Nevertheless, progress in this field is hindered by the inherent complexity of emotions, cultural differences in expression, and technical limitations that make accurate detection challenging. This paper delivers a broad review of contemporary approaches to emotion recognition. It highlights techniques based on facial expression analysis (FER), oculometrics (OM), microexpressions identification (MER), and speech analysis (SER). Further attention is given to methods involving body posture, gesture, and gait, as well as tactile interaction, text-based emotion recognition, and methods based on self-reporting. In addition, physiological signal-driven methods are discussed in depth, including respiration signals (RS), galvanic skin response (GSR), electroencephalography (EEG), electromyography (EMG), skin temperature (SKT), cardiac signals (ECG, PPG, HRV), and touch dynamics (TD) analysis. This comprehensive overview lays the foundation for advancing research on machine-based emotion recognition.