Face Recognition Using Python

This studies paper explores improvements in face reputation technology, emphasizing its applications, challenges, and ethical concerns. With the fast evolution contemporary deep state- of-the-art techniques, especially convolutional neural networks (CNNs), face reputation structures have performed huge upgrades in accuracy and pace. This take a look at provides a complete overview latest contemporary methodologies, consisting of feature extraction and classification algorithms, at the same time as comparing traditional techniques to contemporary deep cutting- edge frameworks. We performed a sequence brand new experiments on publicly available datasets to assess the performance trendy numerous face recognition models under diverse conditions, along with versions in lighting fixtures, pose, and occlusion. outcomes indicate that models exhibit splendid robustness, yet troubles inclusive of bias and privacy concerns continue to be accepted.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-03-26
DOI
https://doi.org/10.55041/ijsrem43084
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
0.00
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article

Face Recognition Using Python

B Naga Narendra
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Face recognition and analysis
article

Face Recognition Using Python

B Naga Narendra
article en

Abstract

This studies paper explores improvements in face reputation technology, emphasizing its applications, challenges, and ethical concerns. With the fast evolution contemporary deep state- of-the-art techniques, especially convolutional neural networks (CNNs), face reputation structures have performed huge upgrades in accuracy and pace. This take a look at provides a complete overview latest contemporary methodologies, consisting of feature extraction and classification algorithms, at the same time as comparing traditional techniques to contemporary deep cutting- edge frameworks. We performed a sequence brand new experiments on publicly available datasets to assess the performance trendy numerous face recognition models under diverse conditions, along with versions in lighting fixtures, pose, and occlusion. outcomes indicate that models exhibit splendid robustness, yet troubles inclusive of bias and privacy concerns continue to be accepted.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(03)
Openalex Percentile: Top 96%
Face recognition and analysis
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