Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Automatic Processing and Automatic Social Behavior

Automatic Processing and Automatic Social Behavior

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...

Facial Feedback Hypothesis

Facial Feedback Hypothesis

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 of...

Association Areas of the Cortex

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

You might also read

Related Articles

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

Sort by

Same author

Marfan Syndrome Prediction Via Graph Neural Networks on 3D Facial Cues.

IEEE journal of biomedical and health informatics·2026

Same author

Fingermark visualisation on Euro banknotes - Current practices in European laboratories and outcomes of the 2024 collaborative exercise of the ENFSI Fingerprint Working Group.

Forensic science international·2026

Same author

Association between Klotho levels in cerebrospinal fluid and choroid plexus enlargement in neurodegeneration.

Frontiers in aging neuroscience·2025

Same author

A response to EA-4/23 INF:2025 "The Assessment and Accreditation of Opinions and Interpretations using ISO/IEC 17025:2017".

Forensic science international·2025

Same author

Corrigendum to "STEFA G03 - Joint collaborative exercise for document examination, DNA, fingerprints and handwriting" [Forensic Sci. Int. 370 (2025) 112459].

Forensic science international·2025

Same author

STEFA G03 - Joint collaborative exercise for document examination, DNA, fingerprints and handwriting.

Forensic science international·2025

Search research articles

Related Experiment Video

Updated: May 25, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Improving automatic face recognition with user interaction.

Stefano Arca¹, Paola Campadelli, Raffaella Lanzarotti

¹Dipartimento di Scienze dell'Informazione, Università degli Studi di Milano, Via Comelico, 39/41, 20135 Milano, Italy.

Journal of Forensic Sciences

|January 13, 2012

Summary

This summary is machine-generated.

This study introduces an interactive face recognition system. It allows users to assist the algorithm when errors occur, improving accuracy in challenging scenarios.

More Related Videos

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

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Related Experiment Videos

Last Updated: May 25, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

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

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Computer Science
Biometrics
Human-Computer Interaction

Background:

Current face recognition systems struggle with low-quality images and algorithmic limitations.
Limited user interaction in existing systems hinders performance in uncontrolled environments.

Purpose of the Study:

To develop a guided user interface for face recognition systems.
To enhance system performance by allowing user intervention based on task difficulty and available information.

Main Methods:

Integration of a guided user interface with a pre-existing automatic face recognition algorithm.
Implementation of adjustable interaction levels from fully automatic to fully assisted execution.
Testing on a standard face identification task using the XM2VTS database.

Main Results:

The proposed interface allows users to intervene selectively when automatic recognition fails or intermediate results are unsatisfactory.
Demonstrated improvement in user interaction and overall system performance compared to fully automatic systems.
Validated the system's viability on a public benchmark dataset.

Conclusions:

The developed guided interface enhances face recognition system usability and accuracy.
User-assisted interaction is crucial for overcoming limitations in real-world face recognition applications.
The system offers flexible control, adapting to varying recognition challenges.