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Related Experiment Video

Updated: Dec 31, 2025

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

Face Recognition Systems: A Survey.

Yassin Kortli^1,2, Maher Jridi¹, Ayman Al Falou¹

¹AI-ED Department, Yncrea Ouest, 20 rue du Cuirassé de Bretagne, 29200 Brest, France.

Sensors (Basel, Switzerland)

|January 16, 2020

Summary

This summary is machine-generated.

This survey reviews face recognition techniques, comparing local, holistic, and hybrid methods. It details their pros and cons for applications like video surveillance and autonomous vehicles.

Keywords:

biometric systems face recognition systems person identification survey

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Published on: August 26, 2016

Area of Science:

Computer Science
Artificial Intelligence
Biometrics

Background:

Face recognition technology has seen rapid advancements due to increasing industrial applications.
Key applications include video surveillance, criminal identification, and access control systems.
Current techniques broadly fall into local, holistic, and hybrid approaches.

Purpose of the Study:

To provide a comprehensive review of established face recognition techniques.
To categorize and compare different approaches based on their methodologies.
To analyze the strengths and weaknesses of various face recognition schemes.

Main Methods:

Categorization of face recognition techniques into local, holistic, and hybrid approaches.
Detailed comparison of techniques considering robustness, accuracy, complexity, and discrimination.
Overview of commonly used face recognition databases for supervised and unsupervised learning.

Main Results:

Analysis of advantages and disadvantages for each face recognition category.
Presentation of numerical results for prominent techniques within experimental contexts.
Discussion of challenges encountered and addressed by current methods.

Conclusions:

Face recognition technology is crucial for various industries, with diverse algorithmic approaches.
A thorough understanding of technique trade-offs is essential for optimal system design.
Future research directions are identified to advance the field of face recognition.