From Fingers to Faces: Visual Semiotics and Digital Forensics.

Area of Science:

• Forensic Science
• Semiotics
• Digital Forensics

Background:

• Societal identification is fundamental, particularly in law enforcement, involving a historical tension between concealing and unmasking identities.
• Semiotics provides tools to analyze signs used in societal detection and identification, including forensic and criminological markers.
• Historically, the face has been a primary identifier, but modern forensics shifted to fingerprinting, influenced by Eastern practices, due to the face's unreliability.

Purpose of the Study:

• To explore the role of semiotics in understanding identification processes within law enforcement and forensics.
• To analyze the historical evolution of identification methods, from physical signs to digital technologies.
• To investigate the re-emergence of facial recognition in digital forensics and its inherent sociocultural biases.

Main Methods:

• Historical analysis of identification techniques in criminology.
• Application of semiotic principles to interpret signs of identity and deception.
• Examination of the transition from traditional to digital identification methods, including fingerprinting and facial recognition.
• Critical review of sociocultural biases influencing AI-driven facial recognition.

Main Results:

• The study highlights a dynamic interplay between identity concealment and societal detection throughout history.
• Semiotics offers a framework for deconstructing the signs and symbols of identification and imposture.
• The digital era has seen a resurgence of facial recognition in forensics, raising concerns about embedded biases.
• Facial recognition technology in AI forensics is increasingly scrutinized for its potential to perpetuate sociocultural biases.

Conclusions:

• Semiotics is vital for critically evaluating identification methods in forensics, especially in the digital age.
• The reliance on facial recognition necessitates a deep understanding of its semiotic and sociocultural implications.
• Addressing biases in AI-driven facial recognition is crucial for equitable and effective law enforcement identification.