Biometric Coherence and the Science of Trust

The Concept of Coherence

At the heart of human experience is coherence—the principle that living systems respond systematically to their environments. When a loud sound erupts, heart rate slows, pupils dilate, and facial muscles tense in unison. When a bright light suddenly flashes, pupils constrict reflexively in a fraction of a second. These responses are not isolated quirks of physiology but are tightly coupled signatures of a living nervous system interacting with the world.

This concept of coherence is critical for digital trust. Detecting individual physiological signals—such as a heartbeat or a blink—can be useful, but in isolation they are not sufficient. What matters is whether those signals occur in synchrony with one another and with external events. A video may show the illusion of a pulse, but unless that pulse varies in time with real-world conditions, it fails the test of coherence. Moveris builds on this principle by formalizing biometric coherence as a measure of whether observed physiological signals genuinely reflect the embodied responses of a living person.

Stimulus–Response Coupling in Human Systems

The human nervous system is built to process and respond to stimuli. Decades of psychophysiological research have documented reliable links between environmental inputs and physiological outputs. For example, heart rate deceleration occurs consistently when individuals orient to novel or significant stimuli, reflecting a rapid shift of attentional resources (Fisher et al., 2018). Similarly, facial muscle activity reveals underlying affective states in response to emotional cues, often before individuals are consciously aware of their reactions (Cacioppo et al., 1986).

Theoretical frameworks such as the Limited Capacity Model of Motivated Mediated Message Processing (LC4MP) provide a scientific foundation for these patterns. LC4MP posits that humans allocate finite cognitive resources to process mediated messages, and that psychophysiological signals serve as real-time indicators of where those resources are directed (Lang, 2009). In practice, this means that physiological responses are not random noise; they follow predictable patterns in relation to environmental demands.

In this sense, coherence is not a metaphor but a measurable phenomenon. Living humans demonstrate entrainment, synchrony, and resonance with external inputs. Their physiological systems oscillate in time with media rhythms, shift together during moments of suspense, and amplify in response to emotionally charged events. Synthetic agents may be able to mimic isolated features, but they lack the capacity to produce this integrated, stimulus-driven coherence.

Evidence from Media Psychophysiology

Media psychophysiology has provided a robust body of evidence demonstrating coherence in action. Research on heart rate, for instance, shows reliable deceleration during the orienting response, a marker of attention to novel or important stimuli (Fisher et al., 2018). Studies of facial electromyography (EMG) reveal that subtle muscle activity distinguishes positive from negative affect even when expressions are consciously suppressed (Cacioppo et al., 1986). Pupillometry adds another layer, with dilation reliably indexing both emotional arousal and cognitive load (Beatty & Lucero-Wagoner, 2000).

Critically, these signals rarely shift in isolation. During emotionally intense scenes, heart rate, skin conductance, and facial EMG often change together, creating a multimodal pattern of coherence (Ravaja, 2004). These patterns are robust across individuals and contexts, providing a strong scientific basis for using coherence as a marker of authentic human engagement.

What emerges from this research is a clear principle: physiological signals are not independent artifacts to be cherry-picked for detection, but integrated components of a living system that operates in synchrony with environmental demands.

Applying Coherence to Deepfake Detection

The coherence principle provides a formidable barrier to synthetic media. Deepfakes can simulate surface-level behaviors—such as smiling, blinking, or even a visible pulse—but they struggle to produce stimulus-locked coherence across multiple physiological channels. A synthetic face may “smile,” but without the micro-activations of corrugator and zygomaticus muscles, the smile lacks physiological authenticity. A replayed video may show pupils changing size, but they will not constrict in perfect synchrony with actual changes in screen brightness.

Generative systems can attempt to fake individual cues, but coordinating multiple signals across time and in response to dynamic environmental stimuli requires a living nervous system. This makes coherence uniquely resistant to forgery and places it at the center of next-generation liveness detection.

The Moveris Approach: Coherence Scoring

Moveris operationalizes biometric coherence through a multimodal analysis pipeline. The process begins with the extraction of stimulus features from the environment, including audiovisual properties such as changes in brightness, scene cuts, and auditory intensity. Simultaneously, psychophysiological signals are captured via webcam-based algorithms: heart rate variability from rPPG, pupillary dynamics from ocular analysis, and facial micro-movements from computer vision.

These data streams are then analyzed for temporal synchrony. Cross-correlation techniques and time-series models are used to determine whether physiological responses align with expected stimulus events. The outputs are integrated into a composite coherence score, reflecting the likelihood that the observed responses belong to a living human.

This approach transforms coherence from a theoretical principle into an applied measure of trust. It not only detects human presence but also explains it, offering interpretable evidence of why a subject was classified as authentic or synthetic.

Implications for Theory and Practice

The biometric coherence framework advances both scientific and applied domains. Theoretically, it extends LC4MP by quantifying stimulus–response synchrony as a marker of human-system interaction. Practically, it equips industries with a tool for resilient trust.

In financial technology, coherence strengthens KYC/AML processes by grounding verification in physiological reality rather than spoofable surface features. In media verification, coherence enables platforms to authenticate both live and recorded content, providing a defense against synthetic manipulation. In security and access control, it ensures that systems admit only those who demonstrate the embodied signatures of life.

By operationalizing coherence, Moveris bridges the gap between decades of psychophysiological research and the urgent need for scalable digital trust.

Conclusion

Trust in digital interactions cannot rely on isolated signals or static identifiers. The future lies in coherence—the integrated, stimulus-driven patterns that only living humans produce. By combining theoretical insights from psychophysiology with applied signal processing, Moveris introduces biometric coherence as a foundation for liveness detection. This approach resists spoofing not by outpacing synthetic generation, but by grounding verification in the irreducible biological synchrony of human life.

References (Selected for this Paper)

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• Cacioppo, J. T., Petty, R. E., Losch, M. E., & Kim, H. S. (1986). Electromyographic activity over facial muscle regions can differentiate the valence and intensity of affective reactions. Journal of Personality and Social Psychology, 50(2), 260–268.
  
  
• Cacioppo, J. T., Tassinary, L. G., & Berntson, G. G. (Eds.). (2017). Handbook of psychophysiology (4th ed.). Cambridge University Press.
  
  
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• Lang, A. (2009). The limited capacity model of motivated mediated message processing. In R. Nabi & M. Oliver (Eds.), The SAGE handbook of media processes and effects (pp. 193–204). Sage.
  
  
• Ravaja, N. (2004). Contributions of psychophysiology to media research: Review and recommendations. Media Psychology, 6(2), 193–235.
  
  
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