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Neural Computing and Applications

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02-03-2022 | Original Article

Heterogeneous face quality assessment

Authors: Shubhobrata Bhattacharya, Aurobinda Routray

Published in: Neural Computing and Applications | Issue 14/2022

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Abstract

The Face Recognition (FR) research has attained a mature stage in the last decade. However, evaluating face quality before recognition can enhance the performance of these algorithms. We proposed a new Face Quality Assessment (FQA) algorithm for face images of multiple modalities and examined its effects on Heterogeneous Face Recognition in this paper (HFR). The proposed work comprises two segments. In the first part of the work, an image descriptor is proposed. The descriptor captures the quality-sensitive features. It is named Local Quality Descriptor (LQD). The paper then proposed a model for measuring the quality from the LQD feature using a subspace projection. The Face Quality Score (FQS) obtained is scaled between 0 and 100. The effects of the gallery and probe image quality on the recognition algorithm’s performance are also discussed in the paper. The proposed model was trained with the data with quality variations. These variations are generated with factors like blurring, Gaussian white noise, low illumination, etc. The Face Quality Confidence Score is computed by considering the quality of the probe and gallery images (FQCS). The impacts of FQS and FQCS on face recognition and face verification are studied with publicly available databases, namely: CASIA, FERET, LDHF, and QDF.

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Bhattacharya S, Nainala GS, Rooj S, Routray A (2019) Local force pattern (lfp): Descriptor for heterogeneous face recognition. Patt Recognit Lett 125:63–70CrossRef

Roy H, Bhattacharjee D (2016) Local-gravity-face (lg-face) for illumination-invariant and heterogeneous face recognition. IEEE Trans Inf Foren Sec 11(7):1412–1424CrossRef

Duan Y, Lu J, Feng J, Zhou J (2018) Context-aware local binary feature learning for face recognition. IEEE Trans Patt Analy Mach Intell 40(5):1139–1153CrossRef

Ouyang S, Hospedales T, Song Y-Z, Li X, Loy CC, Wang X (2016) A survey on heterogeneous face recognition: Sketch, infra-red, 3d and low-resolution. Image and Vis Comput 56:28–48CrossRef

Ahonen T, Hadid A, Pietikainen M (2006) Face description with local binary patterns: Application to face recognition. IEEE Trans Patt Analy Mach Intell 12:2037–2041CrossRef

Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Process 11(4):467–476CrossRef

Zhang J, Yan Y, Lades M (1997) Face recognition: eigenface, elastic matching, and neural nets. Proc IEEE 85(9):1423–1435CrossRef

Wang Z, Bovik AC, Sheikh HR, Simoncelli EP et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Proc 13(4):600–612CrossRef

Bhattacharya S, Routray A (2017) Score based face quality assessment (fqa). In: 2017 14th IEEE India Council International Conference (INDICON). IEEE 2017:1–6

10.

Nasrollahi K, Moeslund TB (2009) Complete face logs for video sequences using face quality measures. IET Signal Proc 3(4):289–300CrossRef

11.

Sellahewa H, Jassim SA (2010) Image-quality-based adaptive face recognition. IEEE Trans Instrum Meas 59(4):805–813CrossRef

12.

Chen J, Deng Y, Bai G, Su G (2014) Face image quality assessment based on learning to rank. IEEE Signal Proc Lett 22(1):90–94CrossRef

13.

Yu J, Sun K, Gao F, Zhu S (2018) Face biometric quality assessment via light cnn. Patt Recog Lett 107:25–32CrossRef

14.

Vignesh S, Priya K M, Channappayya S S (2015) Face image quality assessment for face selection in surveillance video using convolutional neural networks. In: 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, pp. 577–581

15.

Best-Rowden L, Jain AK (2018) Learning face image quality from human assessments. IEEE Trans Inf Foren Sec 13(12):3064–3077CrossRef

16.

Dutta A, Veldhuis R, Spreeuwers L (2015) Predicting face recognition performance using image quality. arXiv preprint arXiv:1510.07119

17.

Beveridge J R, Phillips P J, Givens G H, Draper B A, Teli M N, Bolme D S (2011) When high-quality face images match poorly. IEEE

18.

Phillips P J, Beveridge J R, Bolme D S, Draper B A, Givens G H, Lui Y M, Cheng S, Teli M N, Zhang H (2013) On the existence of face quality measures. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS). IEEE, pp. 1–8

19.

Yang J, Ren P, Zhang D, Chen D, Wen F, Li H, Hua G (2017) Neural aggregation network for video face recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4362–4371

20.

Tran L, Yin X, Liu X (2018) Representation learning by rotating your faces. IEEE Trans Patt Analy Mach Intell 41(12):3007–3021CrossRef

21.

Bhattacharya S, Dasgupta A, Routray A (2016) Robust face recognition of inferior quality images using local gabor phase quantization. In: 2016 IEEE Students’ Technology Symposium (TechSym). IEEE 2017:294–298

22.

Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2009) Wld: A robust local image descriptor. IEEE Trans Patt Analy Mach Intell 32(9):1705–1720CrossRef

23.

Lai Z-R, Dai D-Q, Ren C-X, Huang K-K (2015) Multiscale logarithm difference edgemaps for face recognition against varying lighting conditions. IEEE Trans Image Proc 24(6):1735–1747MathSciNetCrossRef

24.

Wang B, Li W, Yang W, Liao Q (2011) Illumination normalization based on weber’s law with application to face recognition. IEEE Signal Proc Lett 18(8):462–465CrossRef

25.

Gong Y, Lazebnik S, Gordo A, Perronnin F (2012) Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. IEEE Trans Patt Analy Mach Intell 35(12):2916–2929CrossRef

26.

Tax DM, Duin RP (1999) Support vector domain description. Patt Recog lett 20(11–13):1191–1199CrossRef

27.

Lee S-W, Park J, Lee S-W (2006) Low resolution face recognition based on support vector data description. Patt Recog 39(9):1809–1812CrossRef

28.

Vapnik V (2013) The nature of statistical learning theory. Springer science & business media, GermanyMATH

29.

Grother P, Tabassi E (2007) Performance of biometric quality measures. IEEE Trans Patt Analy Mach Intell 29(4):531–543CrossRef

30.

Marr D (1982) Vision: A computational investigation into the human representation and processing of visual information

31.

Chen W, Er MJ, Wu S (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst Man, and Cybernetics Part B (Cybernetics) 36(2):458–466CrossRef

32.

Niu Y, Zhao Y, Ni R (2017) Robust median filtering detection based on local difference descriptor. Signal Proc: Image Commun 53:65–72

33.

Lei Z, Pietikäinen M, Li SZ (2013) Learning discriminant face descriptor. IEEE Trans Patt Analy Mach Intell 36(2):289–302

34.

Lu J, Liong VE, Zhou X, Zhou J (2015) Learning compact binary face descriptor for face recognition. IEEE Trans Patt Analy Mach Intell 37(10):2041–2056CrossRef

35.

Pan Z, Fan H, Zhang L (2015) Texture classification using local pattern based on vector quantization. IEEE Trans Image Proc 24(12):5379–5388MathSciNetCrossRef

36.

Li J, Sang N, Gao C (2016) Local fractional order derivative vector quantization pattern for face recognition. In: Asian Conference on Computer Vision. Springer, pp. 234–247

37.

Nasrollahi K, Moeslund TB (2011) Extracting a good quality frontal face image from a low-resolution video sequence. IEEE Trans Circ Syst Video Technol 21(10):1353–1362CrossRef

38.

Hernandez-Ortega J, Galbally J, Fierrez J, Haraksim R, Beslay L (2019) Faceqnet: Quality assessment for face recognition based on deep learning. arXiv preprint arXiv:1904.01740

39.

Phillips PJ, Wechsler H, Huang J, Rauss PJ (1998) The feret database and evaluation procedure for face-recognition algorithms. Image and Vision Comput 16(5):295–306CrossRef

40.

Li S, Yi D, Lei Z, Liao S (2013) The casia nir-vis 2.0 face database. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 348–353

41.

Kang D, Han H, Jain AK, Lee S-W (2014) Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching. Patt Recog 47(12):3750–3766CrossRef

42.

Bhattacharya S, Rooj S, Routray A (2019) Qdf: A face database with varying quality. Signal Process: Image Commun 74:13–20

43.

Xiao Y, Cao Z, Wang L, Li T (2017) Local phase quantization plus: A principled method for embedding local phase quantization into fisher vector for blurred image recognition. Inf Sci 420:77–95CrossRef

Title: Heterogeneous face quality assessment
Authors: Shubhobrata Bhattacharya
Aurobinda Routray
Publication date: 02-03-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 14/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07045-3

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