Can holistic representations be used for face biometric quality assessment

doi:10.1109/ICIP.2013.6738575

Home
/
Papers
/
Can holistic representations be used for face biometric quality assessment

Proceedings Article•DOI•

Can holistic representations be used for face biometric quality assessment

Samarth Bharadwaj¹, Mayank Vatsa¹, Richa Singh¹•Institutions (1)

Indraprastha Institute of Information Technology¹

01 Sep 2013-pp 2792-2796

TL;DR: This paper investigates the use of holistic super-ordinate representations, namely, Gist and sparsely pooled Histogram of Orientated Gradient, in classifying images into different quality categories that are derived from matching performance.

read less

Abstract: A face quality metric must quantitatively measure the usability of an image as a biometric sample. Though it is well established that quality measures are an integral part of robust face recognition systems, automatic measurement of bio-metric quality in face is still challenging. Inspired by scene recognition research, this paper investigates the use of holistic super-ordinate representations, namely, Gist and sparsely pooled Histogram of Orientated Gradient (HOG), in classifying images into different quality categories that are derived from matching performance. The experiments on the CAS-PEAL and SCFace databases containing covariates such as illumination, expression, pose, low-resolution and occlusion by accessories, suggest that the proposed algorithm can efficiently classify input face image into relevant quality categories and be utilized in face recognition systems.

...read moreread less

Citations

PDF

Open Access

More filters

Journal Article•DOI•

Biometric quality: a review of fingerprint, iris, and face

[...]

Samarth Bharadwaj¹, Mayank Vatsa¹, Richa Singh¹•Institutions (1)

Indian Institutes of Information Technology¹

02 Jul 2014-Eurasip Journal on Image and Video Processing

TL;DR: The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.

...read moreread less

Abstract: Biometric systems encounter variability in data that influence capture, treatment, and u-sage of a biometric sample. It is imperative to first analyze the data and incorporate this understanding within the recognition system, making assessment of biometric quality an important aspect of biometrics. Though several interpretations and definitions of quality exist, sometimes of a conflicting nature, a holistic definition of quality is indistinct. This paper presents a survey of different concepts and interpretations of biometric quality so that a clear picture of the current state and future directions can be presented. Several factors that cause different types of degradations of biometric samples, including image features that attribute to the effects of these degradations, are discussed. Evaluation schemes are presented to test the performance of quality metrics for various applications. A survey of the features, strengths, and limitations of existing quality assessment techniques in fingerprint, iris, and face biometric are also presented. Finally, a representative set of quality metrics from these three modalities are evaluated on a multimodal database consisting of 2D images, to understand their behavior with respect to match scores obtained from the state-of-the-art recognition systems. The analysis of the characteristic function of quality and match scores shows that a careful selection of complimentary set of quality metrics can provide more benefit to various applications of biometric quality.

...read moreread less

119 citations

Cites background from "Can holistic representations be use..."

...Recently, holistic descriptors extracted from the face region are shown to be good indicators of performance of face recognition systems [87]....
[...]

Journal Article•DOI•

Learning Face Image Quality From Human Assessments

[...]

Lacey Best-Rowden¹, Anil K. Jain¹•Institutions (1)

Michigan State University¹

30 Jan 2018-IEEE Transactions on Information Forensics and Security

TL;DR: This is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.

...read moreread less

Abstract: Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this paper, we propose (and compare) two methods for learning face image quality based on target face quality values from: 1) human assessments of face image quality (matcher-independent) and 2) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, Labeled Faces in the Wild and IARPA Janus Benchmark-A (IJB-A), which both contain facial variations encompassing a multitude of quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the false non-match rate at 1% false match rate by at least 13% for two face matchers (a commercial off-the-shelf matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To the best of our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.

...read moreread less

79 citations

Posted Content•

Face Image Quality Assessment: A Literature Survey

[...]

Torsten Schlett, Christian Rathgeb, Olaf Henniger, Javier Galbally, Julian Fierrez, Christoph Busch - Show less +2 more

02 Sep 2020-arXiv: Computer Vision and Pattern Recognition

TL;DR: This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input and a trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches.

...read moreread less

Abstract: The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to filter out low quality data. This survey provides an overview of the face quality assessment literature in the framework of face biometrics, with a focus on face recognition based on visible wavelength face images as opposed to e.g. depth or infrared quality assessment. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.

...read moreread less

51 citations

Cites background or methods from "Can holistic representations be use..."

...LFW [79] 2011 to 2021 17: B [181][9][23] E [113][198][120][46][47] [25][123][140][24][73][22][176][166][193] FERET [146] 2007 to 2020 9: B [3][76][23][189] C [176] E [2][1][120] [160] VGGFace2 [21] 2019 to 2021 7: B [73][74] C [191] E [46][47][24][193] CASIA-WebFace [197] 2017 to 2021 7: B [198][204][166] C [140][9] E [113][193] CAS-PEAL [55] 2009 to 2018 6: B [2][76][17] C [1][184] E [201] FRGC [145] 2006 to 2018 6: B [102][101][75][23] C [152][184] MS-Celeb-1M [68] 2019 to 2020 5: B [166] C [22][176][193] E [113] CFP [164] 2019 to 2021 5: E [25][123][24][22][166] IJB-C [122] 2019 to 2021 5: E [123][140][191][22][166] YTF [188] 2014 to 2020 5: B [136] E [198][30][22][166] MS1MV2 [34] 2021 4: C [25][123][140][24] IJB-A [105] 2017 to 2019 4: B [113] C [193] E [166][9] ChokePoint [189] 2011 to 2018 4: B [150][179] E [184][189] SCface [64] 2011 to 2018 4: B [17] E [120][184][23] Extended Yale [110] 2010 to 2018 4: B [153][151][163] C [184] CPLFW [205] 2021 3: E [25][123][24] IJB-B [187] 2021 3: E [25][123][24] Adience [40] 2020 to 2021 3: E [25][140][176] BioSecure [139] 2019 to 2021 3: E [46][73][74] GBU [143] 2012 to 2014 3: B [2][1] E [142] AT&T [158] 2010 to 2016 3: B [76][163] C [152] CMU-PIE [167] 2009 to 2011 3: C [11] E [160][189] FRVT 2006 [147] 2008 to 2010 3: E [11][13][14] Yale [57] 2007 to 2014 3: B [2][1] E [56] BANCA [8] 2006 to 2008 3: B [108][109] E [156] AgeDB [129] 2021 2: E [123][24] CALFW [206] 2021 2: E [123][24] MEDS-II [44] 2019 to 2020 2: B [155][154] MegaFace [99] 2019 to 2020 2: E [22][166] AR [121] 2014 to 2018 2: C [152][184] PaSC [12] 2013 to 2018 2: B [150] E [142] MBGC [144] 2012 to 2014 2: E [2][1] Q-FIRE [95] 2012 to 2014 2: E [2][77]...
[...]
...[17] trained a one-vs-all SVM for 4 quality bins using either sparsely pooled Histogram of Oriented Gradient (HOG) or Gist [137] input features....
[...]
...• FR-based ground truth training (Dfrt): These approaches obtained training data from FRmodels [17][179][76][9] [150][74][73][191][140][25]....
[...]
...2013 [17] Dfrt 4-class SVM on Gist[137] or HOG....
[...]
...Some of the works evaluated FIQA performance exclusively by means other than the ERC - for example, FR performance was evaluated for 4 FIQA-derived quality bins in [17]....
[...]

Journal Article•DOI•

Face biometric quality assessment via light CNN

[...]

Jun Yu¹, Kejia Sun¹, Fei Gao¹, Suguo Zhu¹•Institutions (1)

Hangzhou Dianzi University¹

01 Aug 2017-Pattern Recognition Letters

TL;DR: This paper considered five categories of common homogeneous distortion in video suvillance applications, i.e. low-resolution, blurring, additive Gaussian white noise, salt and pepper noise, and Poisson noise and proposed a novel biometric quality assessment (BQA) method for face images and explored its applications in face recognition.

...read moreread less

31 citations

Journal Article•

Standardization of Face Image Sample Quality

[...]

LI Zi-qing

01 Jan 2009-Journal of Chinese Computer Systems

TL;DR: This paper proposes an approach for standardization of facial image quality, and develops facial symmetry based methods for the assessment of it by measuring facial asymmetries caused by non-frontal lighting and improper facial pose.

...read moreread less

Abstract: Performance of biometric systems is dependent on quality of acquired biometric samples. Poor sample quality is a main reason for matching errors in biometric systems and may be the main weakness of some implementations. This paper proposes an approach for standardization of facial image quality, and develops facial symmetry based methods for the assessment of it by measuring facial asymmetries caused by non-frontal lighting and improper facial pose. Experimental results are provided to illustrate the concepts, definitions and effectiveness.

...read moreread less

28 citations

References

PDF

Open Access

More filters

Book Chapter•DOI•

Asymmetry-Based Quality Assessment of Face Images

[...]

Guangpeng Zhang¹, Yunhong Wang¹•Institutions (1)

Beihang University¹

26 Nov 2009

TL;DR: Three face quality measures are proposed to solve the incapability for performance prediction and remove the requirement for scale normalization of existing methods, using SIFT to extract scale insensitive feature points on face images.

...read moreread less

Abstract: Quality assessment plays an important role in biometrics field. Unlike the popularity of fingerprint and iris quality assessment, the evaluation of face quality is just started. To solve the incapability for performance prediction and remove the requirement for scale normalization of existing methods, three face quality measures are proposed in this paper. SIFT is utilized to extract scale insensitive feature points on face images, and three asymmetry-based quality measures are calculated by applying different constraints. Systematical experiments validate the efficacy of the proposed quality measures.

...read moreread less

38 citations

Journal Article•DOI•

FRVT 2006: Quo Vadis face quality

[...]

J. Ross Beveridge¹, Geof H. Givens¹, P. Jonathon Phillips², Bruce A. Draper¹, David S. Bolme¹, Yui Man Lui¹ - Show less +2 more•Institutions (2)

Colorado State University¹, National Institute of Standards and Technology²

01 May 2010-Image and Vision Computing

TL;DR: This finding shows that simple measurable factors are capable of characterizing face quality; however, these factors typically interact with both algorithm and setting.

...read moreread less

36 citations

Proceedings Article•DOI•

When high-quality face images match poorly

[...]

J. Ross Beveridge¹, P. Jonathon Phillips, Geof H. Givens¹, Bruce A. Draper¹, Mohammad Nayeem Teli¹, David S. Bolme¹ - Show less +2 more•Institutions (1)

Colorado State University¹

21 Mar 2011

TL;DR: This paper presents a relational graph-based evaluation technique that uses match scores produced by face recognition algorithms to determine the “quality” of images, and demonstrates that only a small fraction of the images in a well-studied data set (FRVT 2006) are low-quality images.

...read moreread less

Abstract: 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.

...read moreread less

30 citations

"Can holistic representations be use..." refers methods in this paper

...The effects of resolution and capture conditions, with an analysis of subjective and objective covariates of face biometric in Face Recognition Vendor Test (FRVT) 2006 is presented in [4, 5]....
[...]

Remote Sensing Image Processing. Synthesis Lectures on Image, Video, and Multimedia Processing.

[...]

Gustavo Camps-Valls, Devis Tuia, Luis Gómez-Chova, Sandra Jiménez, Jesús Malo - Show less +1 more

01 Jan 2011

TL;DR: Reference EPFL-BOOK-174404 URL: http://www.morganclaypool.com/toc/ivm/1/1 Record created on 2012-01-24, modified on 2016-08-09.

...read moreread less

Abstract: Reference EPFL-BOOK-174404 URL: http://www.morganclaypool.com/toc/ivm/1/1 Record created on 2012-01-24, modified on 2016-08-09

...read moreread less

28 citations