Robust Metric and Alignment for Profile-Based Face Recognition: An Experimental Comparison

Pan, Gang; Zheng, Lei; Wu, Zhaohui

doi:10.1109/acvmot.2005.105

Cited by 9 publications

(1 citation statement)

References 28 publications

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“…The χ 2 distance is used to identification between two profile projections, represented explicitly. Assuming f and g are two profiles projection, and each element in f has been Corresponding to g ,k = 0,1,2,3,4 …….n where n is number of profile projection points [5].…”

Section: Extraction the Profile From A Frontal Viewmentioning

confidence: 99%

Human Face Identification from Profile Projection

Al-Mubarak

2017

JNUS

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This paper presents face recognition from front face. The idea is to extract the facial profile from a frontal view to provide a measurement for automatic face recognition. The profile is derived from the intensity of horizontal projection of the face image and using profile measure such χ 2 distance for face identification. Three profile projections from 4 images or more are taken for each person, average values are calculated and stored in a database file. Profile measurements are compared with other profile in the database file. Results of experiments to the faces with or without spectacles rotate head (lift, right) and different face expression show that the proposed approach is robust and efficient way of face matching.

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Section: Extraction the Profile From A Frontal Viewmentioning

confidence: 99%

Human Face Identification from Profile Projection

Al-Mubarak

2017

JNUS

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Face Profile Biometric Systems: An Overview

Alamri,

Mahmoodi

2016

Face Recognition Across the Imaging Spectrum

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A Survey on Automatic Face Recognition Using Side‐View Face Images

Santemiz,

Spreeuwers,

Veldhuis

2024

IET Biometrics

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Face recognition from side‐view positions poses a considerable challenge in automatic face recognition tasks. Pose variation up to the side‐view is an issue of difference in appearance and visibility since only one eye is visible at the side‐view poses. Traditionally overlooked, recent advancements in deep learning have brought side‐view poses to the forefront of research attention. This survey comprehensively investigates methods addressing pose variations up to side‐view and categorizes research efforts into feature‐based, image‐based, and set‐based pose handling. Unlike existing surveys addressing pose variations, our emphasis is specifically on extreme poses. We report numerous promising innovations in each category and contemplate the utilization and challenges associated with side‐view. Furthermore, we introduce current datasets and benchmarks, conduct performance evaluations across diverse methods, and explore their unique constraints. Notably, while feature‐based methods currently stand as the state‐of‐the‐art, our observations suggest that cross‐dataset evaluations, attempted by only a few researchers, produce worse results. Consequently, the challenge of matching arbitrary poses in uncontrolled settings persists.

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Robust Metric and Alignment for Profile-Based Face Recognition: An Experimental Comparison

Abstract: Abstract

Cited by 9 publications

References 28 publications

Human Face Identification from Profile Projection

Human Face Identification from Profile Projection

Face Profile Biometric Systems: An Overview

A Survey on Automatic Face Recognition Using Side‐View Face Images

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