Difficult imaging covariates or difficult subjects? - An empirical investigation

Paone, Jeffrey R.; Biswas, Soma; Aggarwal, Gaurav; Flynn, Patrick J.

doi:10.1109/ijcb.2011.6117551

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…Next, the contribution of other variates (such as gender, race, glasses) to the performance degradation of the face recognition system under the ageing effect has been evaluated. Previous studies [11,9,4,10] evaluated the recognition difficulty for facial biometrics to the imaging conditions, such as glasses, illumination etc., and subject covariates, such as gender, race etc., when the ageing effect is either absent or not very profound. None of these studies are conducted on the facial ageing databases.…”

Section: Performance Evaluation and Covariate Analysismentioning

confidence: 99%

“…However their study related to ageing variate (old and young) was not performed on the facial ageing database. Recent study by Paone et al [11] on 659 subjects from FRGC dataset for commercial FaceVACS indicated smiling expression, controlled illumination as favorable covariates without any gender preference. In accordance with the existing studies, our results suggest that covariate influence do exist even under the ageing effect.…”

Section: Comparison With Previous Studies On Covariate Analysismentioning

confidence: 99%

“…Recognition difficulty based on facial biometric is linked to various factors (variate) such as illumination, expression, glasses, gender, race etc [11,9,4,10]. However another variate (factor) gaining increasing attention is the ageing effect.…”

Section: Introductionmentioning

confidence: 99%

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Face Recognition under Ageing Effect: A Comparative Analysis

Akhtar

Rattani

Hadid

et al. 2013

Image Analysis and Processing – ICIAP 2013

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Previous studies indicate that performance of the face recognition system severely degrades under the ageing effect. Despite the rising attention to facial ageing, there exist no comparative evaluation of the existing systems under the impact of ageing. Moreover, the compound effect of ageing and other variate such as glasses, gender etc, that are known to influence the performance, remain overlooked till date. To this aim, the contribution of this work are as follows: 1) evaluation of six baseline facial representations, based on local features, under the ageing effect, and 2) analysis of the compound effect of ageing with other variates, i.e., race, gender, glasses, facial hair etc.

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Section: Performance Evaluation and Covariate Analysismentioning

confidence: 99%

Section: Comparison With Previous Studies On Covariate Analysismentioning

confidence: 99%

See 1 more Smart Citation

Face Recognition under Ageing Effect: A Comparative Analysis

Akhtar

Rattani

Hadid

et al. 2013

Image Analysis and Processing – ICIAP 2013

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“…Previous studies have analyzed many covariates effects on face recognition performance [1], [7], [30], [34]. However, most of them are outdated, and there are several reasons why a new study on these covariates is needed.…”

Section: Introductionmentioning

confidence: 99%

An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification

Lu,

Chen,

Castillo

et al. 2018

Preprint

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Covariates are factors that have a debilitating influence on face verification performance. In this paper, we comprehensively study two covariate related problems for unconstrained face verification: first, how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem; second, how to utilize covariates to improve verification performance. To study the first problem, we implement five state-of-the-art deep convolutional networks (DCNNs) for face verification and evaluate them on three challenging covariates datasets. In total, seven covariates are considered: pose (yaw and roll), age, facial hair, gender, indoor/outdoor, occlusion (nose and mouth visibility, eyes visibility, and forehead visibility), and skin tone. These covariates cover both intrinsic subject-specific characteristics and extrinsic factors of faces. Some of the results confirm and extend the findings of previous studies, others are new findings that were rarely mentioned previously or did not show consistent trends. For the second problem, we demonstrate that with the assistance of gender information, the quality of a pre-curated noisy large-scale face dataset for face recognition can be further improved. After retraining the face recognition model using the curated data, performance improvement is observed at low False Acceptance Rates (FARs) (FAR=10 −5 , 10 −6 , 10 −7 ).

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“…In this paper, we show that impostor scores are also influenced by image quality (like pose, noise, blur, etc). The authors of [51] have also concluded that facial uniqueness (i.e. location in the biometric zoo) changes easily when imaging conditions (like illumination) change.…”

Section: Related Workmentioning

confidence: 99%

Predicting performance of a face recognition system based on image quality

Dutta¹

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Difficult imaging covariates or difficult subjects? - An empirical investigation

Cited by 5 publications

References 15 publications

Face Recognition under Ageing Effect: A Comparative Analysis

Face Recognition under Ageing Effect: A Comparative Analysis

An Experimental Evaluation of Covariates Effects on Unconstrained Face Verification

Predicting performance of a face recognition system based on image quality

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