Constructing Face Image Logs that are Both Complete and Concise

Fourney, Adam; Laganière, Robert

doi:10.1109/crv.2007.20

Cited by 26 publications

(22 citation statements)

References 9 publications

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“…This definition is consistent with most work on biometric quality, and a number of recent papers [8], [16], [5], [18], [17], [7], [4], [10] have looked at general image properties, such as contrast, sharpness, and illumination intensity. Luo [8] presents an instance of a general framework where quality is measured using Radial Basis Function (RBF) without relying on reference images for assessing quality.…”

Section: Introductionsupporting

confidence: 59%

“…Hsu et al [7] showed the consistency and discrepancy between human quality ratings and machine quality scores using a classification-based score normalization process for various quality metrics. Fourney et al [4] define an image's quality based on its potential to lead to a correct identification when used with existing face recognition software. Nasrollahi et al [10] measure quality of faces in video sequences by combining features like outof-plane rotation, sharpness, brightness and resolution.…”

Section: Introductionmentioning

confidence: 99%

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When high-quality face images match poorly

Beveridge

Phillips

Givens

et al. 2011

Face and Gesture 2011

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In face recognition, quality is typically thought of as a property of individual images, not image pairs. The implicit assumption is that high-quality images should be easy to match to each other, while low quality images should be hard to match. This paper presents a relational graph-based evaluation technique that uses match scores produced by face recognition algorithms to determine the "quality" of images. The resulting analysis demonstrates that only a small fraction of the images in a well-studied data set (FRVT 2006) are low-quality images. It is much more common to find relationships in which two images that are hard to match to each other can be easily matched with other images of the same person. In other words, these images are simultaneously both high and low quality. The existence of such contrary images represents a fundamental challenge for approaches to biometric quality that cast quality as an intrinsic property of a single image. Instead it indicates that quality should be associated with pairs of images. In exploring these contrary images, we find a surprising dependence on whether elements of an image pair are acquired at the same location, even in circumstances where one would be tempted to think of the locations as interchangeable. The results presented have important implications for anyone designing face recognition evaluations as well as those developing new algorithms.

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Section: Introductionsupporting

confidence: 59%

Section: Introductionmentioning

confidence: 99%

When high-quality face images match poorly

Beveridge

Phillips

Givens

et al. 2011

Face and Gesture 2011

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show abstract

“…However, to the best of our knowledge, the work published by Laganiere et al [6] is the only one that explicitly addresses the face logging problem building increasing quality image se quences. In that paper, the face quality estimation is given by the linear combination of three different measures: pose esti mation, illumination compensation and sharpness estimation, the last one being studied in the frequency domain.…”

Section: Related Workmentioning

confidence: 98%

A real time solution for face logging

Bimbo

Dini

Lisanti

2009

3rd International Conference on Imaging for Crime Detection and Prevention (ICDP 2009)

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An intrusion logger is a video surveillance application designed to detect intrusion events and document them by storing times tamped images in a log. Face loggers, in particular, are focused in grabbing imagery of intruders face, and are typically used to provide inputs to a face recognition system. Commonly, intru sion loggers are supposed to signal an alarm condition as the intrusion occurs, and are therefore required to run in real time and to work continuously for long time periods.In this paper we present a face logging solution capable of detecting and tracking several targets in real time, grabbing face images and evaluating their quality in order to store only the best for each detected target. An evaluation of the perfor mance on a specifically designed test set is provided.

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“…There are many researches about optimal face acquisition in a surveillance camera or a single sequence of images [1][2][3][4]. Fourney et al [1] proposed a high quality face image selecting method by integrating 6 quality criteria with linear weighting.…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous feature fusion-based optimal face image acquisition in visual sensor network

Lin

Wang

Cui

et al. 2015

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings

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High quality face image acquisition from huge video data obtained in visual sensor network is of great significance in applications related to face processing, such as face recognition and reconstruction. This paper proposes an optimal face image acquisition method in visual sensor network, which is based on collaborative face frames acquisition and heterogeneous feature fusion-based face quality assessment. Gaussian-probabilitydistribution-based multi-view data fusion and kalman filter are used for collaborative target localization and tracking. To achieve primary screening of face frames, a lightweight face frames quality evaluation method is presented. Importantly, new face quality assessment criterion calculation methods are proposed to make fine screening of face images more applicable in visual sensor network. The new face quality assessment criterion calculation methods are based on heterogeneous feature fusion of pedestrian tracking and static face image features analysis. Fuzzy inference engine is used to combine these criteria to generate a face quality assessment score. Experimental results show that the proposed method can acquire optimal face images accurately and robustly.

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Constructing Face Image Logs that are Both Complete and Concise

Cited by 26 publications

References 9 publications

When high-quality face images match poorly

When high-quality face images match poorly

A real time solution for face logging

Heterogeneous feature fusion-based optimal face image acquisition in visual sensor network

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