Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Sources of Self-Esteem III: Social Comparison01:27

Sources of Self-Esteem III: Social Comparison

267
Social comparison plays a fundamental role in the evaluation of personal success and self-worth. Rather than assessing our achievements in isolation, we interpret their significance relative to personal goals and critically in comparison to the performance of others. A grade of B in a mathematics exam might elicit pride if one's expectation was a C, yet result in disappointment if an A was anticipated or if peers achieved superior results. These comparative evaluations illustrate how both...
267
Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

252
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
252
The Sense of Self: Reflected Self-Appraisal and Social Comparison02:57

The Sense of Self: Reflected Self-Appraisal and Social Comparison

56.1K
According to Charles Cooley, we base our image on what we think other people see (Cooley 1902). We imagine how we must appear to others, then react to this speculation. We don certain clothes, prepare our hair in a particular manner, wear makeup, use cologne, and the like—all with the notion that our presentation of ourselves is going to affect how others perceive us. We expect a certain reaction, and, if lucky, we get the one we desire and feel good about it. But more than that, Cooley...
56.1K
Muscles for Facial Expressions01:14

Muscles for Facial Expressions

4.9K
The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...
4.9K
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

411
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
411
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

663
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
663

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Likelihood Ratios for Deep Neural Networks in Face Comparison.

Journal of forensic sciences·2020
Same author

Digital, big data and computational forensics.

Forensic sciences research·2018
Same author

Critical review of the use and scientific basis of forensic gait analysis.

Forensic sciences research·2018
See all related articles

Related Experiment Video

Updated: Feb 2, 2026

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence
11:49

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence

Published on: March 9, 2015

16.7K

Evaluating OpenFace: an open-source automatic facial comparison algorithm for forensics.

Angeliki Fydanaki1, Zeno Geradts1

  • 1Netherlands Forensic Institute, University of Amsterdam, Amsterdam, The Netherlands.

Forensic Sciences Research
|November 29, 2018
PubMed
Summary
This summary is machine-generated.

OpenFace, an open-source deep learning algorithm, shows potential for forensic facial recognition. However, its effectiveness significantly decreases with lower-quality images, limiting current forensic applications.

Keywords:
Forensic sciencesOpenFacedeep learningdigital forensicface comparison

More Related Videos

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

884
Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

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

Published on: December 24, 2015

14.6K

Related Experiment Videos

Last Updated: Feb 2, 2026

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence
11:49

Enhanced Genetic Analysis of Single Human Bioparticles Recovered by Simplified Micromanipulation from Forensic ‘Touch DNA’ Evidence

Published on: March 9, 2015

16.7K
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

884
Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
09:49

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

Published on: December 24, 2015

14.6K

Area of Science:

  • Computer Science
  • Forensic Science
  • Artificial Intelligence

Background:

  • Facial recognition technology is increasingly used in various fields.
  • Open-source deep learning models offer accessible tools for facial analysis.
  • Forensic investigations require high accuracy in facial identification.

Purpose of the Study:

  • To evaluate the applicability of OpenFace models in forensic settings.
  • To assess the impact of image quality on OpenFace's facial recognition performance.
  • To identify limitations and potential improvements for forensic use.

Main Methods:

  • Utilized OpenFace, an open-source deep learning algorithm.
  • Tested models on multiple forensic and public facial datasets (LFW-raw, LFW-deep funnelled, SCface, ForenFace).
  • Analyzed performance based on varying image resolutions and quality.

Main Results:

  • OpenFace's effectiveness degraded significantly with lower input image resolution.
  • Image quality demonstrably impacted the efficiency and accuracy of facial comparisons.
  • Performance variations were consistent across different tested datasets.

Conclusions:

  • OpenFace, in its current state, is inadequate for direct forensic application due to sensitivity to image quality.
  • Further development is needed to enhance robustness for forensic use cases.
  • The technology holds promise for future forensic applications with improvements.