Face Recognition Methods & Applications
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Abstract (arXiv)
Face recognition presents a challenging problem in the field of image analysis and computer vision. The security of information is becoming very significant and difficult. Security cameras are presently common in airports, Offices, University, ATM, Bank and in any locations with a security system. Face recognition is a biometric system used to identify or verify a person from a digital image. Face Recognition system is used in security. Face recognition system should be able to automatically detect a face in an image. This involves extracts its features and then recognize it, regardless of lighting, expression, illumination, ageing, transformations (translate, rotate and scale image) and pose, which is a difficult task. This paper contains three sections. The first section describes the common methods like holistic matching method, feature extraction method and hybrid methods. The second section describes applications with examples and finally third section describes the future research directions of face recognition.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Mar 2014.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 3; 2025: 4.
11 papers.
- 2021Dodging Attack Using Carefully Crafted Natural Makeupcited by 17
- 2022VisCUIT: Visual Auditor for Bias in CNN Image Classifiercited by 8
- 2023New Benchmarks for Asian Facial Recognition Tasks: Face Classification with Large Foundation Modelscited by 4
- 2024Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activenesscited by 4
- 2025DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategycited by 2
- 2021An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHEcited by 1
- 2024Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigationcited by 0
- 2024AdvSecureNet: A Python Toolkit for Adversarial Machine Learningcited by 0
- 2025Unified Face Matching and Physical-Digital Spoofing Attack Detectioncited by 0
- 2025Gradient Norm-based Fine-Tuning for Backdoor Defense in Automatic Speech Recognitioncited by 0
- 2025Pura: An Efficient Privacy-Preserving Solution for Face Recognitioncited by 0
- 2025Pura: An Efficient Privacy-Preserving Solution for Face Recognition21 May 2025
- 2025Gradient Norm-based Fine-Tuning for Backdoor Defense in Automatic Speech Recognition3 Feb 2025
- 2025Unified Face Matching and Physical-Digital Spoofing Attack Detection16 Jan 2025
- 2025DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial Recognition against Dynamic FR Strategy11 Jan 2025
- 2024AdvSecureNet: A Python Toolkit for Adversarial Machine Learning4 Sep 2024
- 2024Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation28 Aug 2024
- 2024Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness30 May 2024
- 2023New Benchmarks for Asian Facial Recognition Tasks: Face Classification with Large Foundation Models15 Oct 2023
- 2022VisCUIT: Visual Auditor for Bias in CNN Image Classifier12 Apr 2022
- 2021Dodging Attack Using Carefully Crafted Natural Makeup14 Sep 2021
- 2021An Improved Real-Time Face Recognition System at Low Resolution Based on Local Binary Pattern Histogram Algorithm and CLAHE15 Apr 2021