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Prosopagnosia

Prosopagnosia

Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...

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Related Experiment Video

Updated: Mar 18, 2026

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm

Published on: December 24, 2015

Human Identification Using Automatic and Semi-Automatically Detected Facial Marks.

Nisha Srinivas¹, Patrick J Flynn¹, Richard W Vorder Bruegge²

¹Department of Computer Science and Engineering, University of Notre Dame, 384 Fitzpatrick Hall, Notre Dame, IN, 46556.

Journal of Forensic Sciences

|July 13, 2016

Summary

This summary is machine-generated.

Facial mark analysis offers an efficient method for identifying individuals in forensic investigations. This study introduces automated and semi-automated systems to improve facial feature comparison for law enforcement.

Keywords:

biometrics facial marks forensic science human identification identity verification semi-automatic system

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Published on: May 7, 2019

Area of Science:

Forensic Science
Computer Vision
Biometrics

Background:

Digital imaging advances enable law enforcement to use image/video analysis for crime investigation.
Manual comparison of facial features for identity determination is inefficient and time-consuming.

Purpose of the Study:

To explore the use of facial marks as unique biometric signatures for individual identification.
To develop automated and semi-automated systems to assist forensic analysts in photographic comparisons.

Main Methods:

Proposed an improved multiscale facial mark system for automatic facial mark detection.
Developed a semi-automatic facial mark system integrating human expertise with automated detection.
Utilized a high-resolution, time-elapsed dataset from the University of Notre Dame (2009-2011).

Main Results:

Demonstrated that geometric distributions of facial mark patterns can effectively distinguish between individuals.
The developed systems showed promise in improving the efficiency of facial feature comparison.

Conclusions:

Facial marks serve as viable biometric signatures for forensic identification.
Automated and semi-automated systems can significantly enhance the process of identity determination from photographic evidence.