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Local image quality measurement for multi-scale forensic palmprints

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Abstract

Numerous studies show that palmprint image quality has a significant effect on every stage of a palmprint recognition system. Although some palmprint image quality measurement(PIQM) methods are proposed, some insufficiency in classification accuracy occurs and attention to detail in measuring local area image quality of multi-scale palmprint images is lacking. On the one hand, the classification accuracy is not very high for 2-class classification and it degrades significantly as the number of classes increases. On the other hand, local area image quality measurement of multi-scale palmprint images has not yet been resolved since the handcrafted features designed through domain knowledge usually works for certain scale image blocks. Meanwhile, the intricate domain knowledge used in the previous methods is difficult for some common users to acquire. In this paper, we propose an end-to-end deep-learning method of strengthening representation ability that learns more abstract, essential, and reliable features to measure the local image quality for multi-scale forensic palmprints. Popular convolutional neural networks (CNNs) are considered because of their powerful representation ability in learning complex features. However, the powerful existing CNNs usually have complex architectures with a large amount of parameters, which need the support of high-performance computers. They are not suitable to be used directly for palmprint image quality assignment and the follow-up palmprint recognition work, which prefers real-time response on commonly available personal computers or even mobile devices. Hence, a new lightweight CNN must be designed to achieve a trade-off between high classification accuracy and practical usability. Considering the attributes of under-processed input images, we reduce the weight of the CNN architecture by reducing the amount of some parameters, and finally a lightweight CNN is designed. As a result, a raw rectangular palmprint image of variable size can be put into the trained model directly and a quality label quickly predicted with high accuracy. After comparison with previous methods, results show that the proposed method can deal with un-pre-processed raw images of a multi-scale input size. Furthermore, it can acquire a richer amount of quality classes with a higher accuracy, which are stable on many different datasets. It also leads to finer and more precise full palmprint image quality maps when compared to previous methods.

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References

  1. Aberni Y, Boubchir L, Daachi B (2017) Multispectral palmprint recognition: a state-of-the-art review. In: 40th International conference on telecommunications and signal processing, TSP 2017, Barcelona, Spain, July 5-7, 2017. IEEE, pp 793–797

  2. Barros RM, Faria BBF, Kuckelhaus SAS (2013) Morphometry of latent palmprints as a function of time. Science and Justice 53(4):402–408

    Google Scholar 

  3. Bianco S, Celona L, Napoletano P, Schettini R (2018) On the use of deep learning for blind image quality assessment. SIViP 12(2):355–362

    Google Scholar 

  4. Bosse S, Maniry D, Müller K-R, Wiegand T, Samek W (2018) Deep neural networks for no-reference and full-reference image quality assessment. IEEE Trans Image Processing 27(1):206–219

    MathSciNet  MATH  Google Scholar 

  5. Cappelli R, Ferrara M, Maltoni D (2010) Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans Pattern Anal Mach Intell 32(12):2128–2141

    Google Scholar 

  6. Cappelli R, Ferrara M, Maltoni D (2011) Fingerprint indexing based on minutia cylinder-code. IEEE Trans Pattern Anal Mach Intell 33(5):1051–1057

    Google Scholar 

  7. Challenge ILSVR. http://www.image-net.org/challenges/LSVRC/

  8. Chen G, Zhai M (2019) Quality assessment on remote sensing image based on neural networks. J Vis Commun Image Represent, 63

  9. Dai J, Zhou J (2011) Multifeature-based high-resolution palmprint recognition. IEEE Trans Pattern Anal Mach Intell 33(5):945–957

    Google Scholar 

  10. Dai J, Feng J, Zhou J (2012) Robust and efficient ridge-based palmprint matching. IEEE Trans Pattern Anal Mach Intell 34(8):1618–1632

    Google Scholar 

  11. El-Sawy A, EL-Bakry H, Loey M (2017) CNN for handwritten Arabic digits recognition based on LeNet-5. In: Hassanien AE, Shaalan K, Gaber T, Azar AT, Tolba MF (eds) Proceedings of the international conference on advanced intelligent systems and informatics 2016. Springer International Publishing, Cham, pp 566–575

    Google Scholar 

  12. Ferrara M, Maltoni D, Cappelli R (2012) Noninvertible minutia cylinder-code representation. IEEE Trans Inform Forens Secur 7(6):1727–1737

    Google Scholar 

  13. Ferrara M, Maltoni D, Cappelli R (2014) A two-factor protection scheme for MCC fingerprint templates. In: Brömme A, Busch C (eds) BIOSIG 2014-proceedings of the 13th international conference of the biometrics special interest group, 10-12, September 2014, Darmstadt, Germany, LNI, vol 230. GI, pp 171–178

  14. Fong C-M, Wang H-W, Kuo C-H, Hsieh P-C (2019) Image quality assessment for advertising applications based on neural network. J Vis Commun Image Represent, 63

  15. Gao F, Wang Y, Li P, Tan M, Yu J, Zhu Y (2017) Deepsim: deep similarity for image quality assessment. Neurocomputing 257:104–114

    Google Scholar 

  16. Gu K, Zhai G, Lin W, Liu M (2016) The analysis of image contrast: from quality assessment to automatic enhancement. IEEE Trans Cybern 46(1):284–297

    Google Scholar 

  17. Hao F, Yang L, Yang G, Liu N, Liu Z (2018) RFPIQM: ridge-based forensic palmprint image quality measurement. IEEE Access 6:62076–62088

    Google Scholar 

  18. He K, Zhang X, Ren S (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition, CVPR 2016, Las Vegas, pp 770–778

  19. Hinton GE, Srivastava N, Krizhevsky A, Sutskever I, Salakhutdinov R (2012) Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580

  20. Hu F, Xia G, Hu J, Zhang L (2015) Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sens 7(11):14680–14707

    Google Scholar 

  21. i-visiongroup http://ivg.au.tsinghua.edu.cn

  22. Jain AK, Feng J (2009) Latent palmprint matching. IEEE Trans Pattern Anal Mach Intell 31(6):1032–1047

    Google Scholar 

  23. Jain AK, Feng J (2011) Latent fingerprint matching. IEEE Trans Pattern Anal Mach Intell 33(1):88–100

    Google Scholar 

  24. Jia S, Zhang Y, Agrafiotis D, Bull DR (2017) Blind high dynamic range image quality assessment using deep learning. In: 2017 IEEE International conference on image processing, ICIP 2017, Beijing, China, September 17-20, pp 765–769

  25. Kang L, Ye P, Li Y, Doermann DS (2014) A deep learning approach to document image quality assessment. In: 2014 IEEE International conference on image processing, ICIP 2014, Paris, France, October 27-30, pp 2570–2574

  26. Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Li F (2014) Large-scale video classification with convolutional neural networks. In: 2014 IEEE Conference on computer vision and pattern recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014. IEEE Computer Society, pp 1725–1732

  27. Kim J, Lee S (2017) Deep learning of human visual sensitivity in image quality assessment framework. In: 2017 IEEE conference on computer vision and pattern recognition, CVPR 2017, Honolulu, HI, USA, July 21-26

  28. Kim J, Zeng H, Ghadiyaram D, Lee S, Zhang L, Bovik AC (2017) Deep convolutional neural models for picture-quality prediction: challenges and solutions to data-driven image quality assessment. IEEE Signal Process Mag 34(6):130–141

    Google Scholar 

  29. Kong AW, Zhang DD, Kamel MS (2009) A survey of palmprint recognition. Pattern Recogn 42(7):1408–1418

    Google Scholar 

  30. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems 25: 26th annual conference on neural information processing systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, pp 1106–1114

  31. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90

    Google Scholar 

  32. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2323

    Google Scholar 

  33. Liang Y, Wang J, Wan X, Gong Y, Zheng N (2016) Image quality assessment using similar scene as reference. In: Leibe et al. (eds) The 14th European conference on computer vision, ECCV 2016, Amsterdam, The Netherlands, October 11-14, pp 3–18

  34. Lim H-t, Kim HG, Ra YM (2018) VR IQA NET: deep virtual reality image quality assessment using adversarial learning. In: 2018 IEEE International conference on acoustics, speech and signal processing, ICASSP 2018, Calgary, AB, Canada, April 15-20, pp 6737–6741

  35. Lin K-Y, Wang G (2018) Hallucinated-iqa: no-reference image quality assessment via adversarial learning. In: 2018 IEEE Conference on computer vision and pattern recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, pp 732–741

  36. Lin K-Y, Wang G (2018) Self-supervised deep multiple choice learning network for blind image quality assessment. In: British machine vision conference 2018, BMVC 2018, Northumbria University, Newcastle, UK, September 3-6, p 70

  37. Liu E, Jain AK, Tian J (2013) A coarse to fine minutiae-based latent palmprint matching. IEEE Trans Pattern Anal Mach Intell 35(10):2307–2322

    Google Scholar 

  38. Liu X, van de Weijer J, Bagdanov AD (2017) Rankiqa: learning from rankings for no-reference image quality assessment. In: IEEE International conference on computer vision, ICCV 2017, Venice, Italy, October 22-29, pp 1040–1049

  39. Ma K, Liu W, Zhang K, Duanmu Z, Wang Z, Zuo W (2018) End-to-end blind image quality assessment using deep neural networks. IEEE Trans Image Process 27(3):1202–1213

    MathSciNet  MATH  Google Scholar 

  40. Medina-Pérez MA, Moreno AM, Ferrer-Ballester MA, García-Borroto M, Loyola-González O, Robles LA (2016) Latent fingerprint identification using deformable minutiae clustering. Neurocomputing 175:851–865

    Google Scholar 

  41. Mocanu DC, Exarchakos G, Liotta A (2014) Deep learning for objective quality assessment of 3d images. In: 2014 IEEE International conference on image processing, ICIP 2014, Paris, France, October 27-30, pp 758–762

  42. Morales A, Medina-Pérez MA, Ferrer-Ballester MA, García-Borroto M, Robles LA (2014) LPIDB v1.0 - latent palmprint identification database. In: IEEE International joint conference on biometrics, Clearwater, IJCB 2014, FL, USA, September 29 -October 2, 2014. IEEE, pp 1–6

  43. Palancar JH, Muñoz-Briseño A, Alonso AG (2014) Using a triangular matching approach for latent fingerprint and palmprint identification. IJPRAI, 28(7)

  44. Peralta D, Triguero I, García S, Saeys Y, Benítez JM, Herrera F (2018) On the use of convolutional neural networks for robust classification of multiple fingerprint captures. Int J Intell Syst 33(1):213– 230

    Google Scholar 

  45. Rajanna U, Erol A, Bebis G (2010) A comparative study on feature extraction for fingerprint classification and performance improvements using rank-level fusion. Pattern Anal Appl 13(3):263–272

    MathSciNet  Google Scholar 

  46. Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651

    Google Scholar 

  47. Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd International conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015 conference track proceedings

  48. Sun Y, Liang D, Wang X, Tang X (2015) Deepid3: face recognition with very deep neural networks. arXiv:1502.00873

  49. Szegedy C, Liu W, Jia Y, Sermanet P, Reed SE, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: IEEE Conference on computer vision and pattern recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015. IEEE Computer Society, pp 1–9

  50. Verdun FR, Racine D, Ott JG, Tapiovaara MJ, Toroi P, Bochud FO, Veldkamp WJH, Schegerer A, Bouwman RW, Hernandez Giron I, Marshall NW, Edyvean S (2015) Image quality in CT: from physical measurements to model observers. Physica Medica-European Journal Of Medical Physics 31(8):823–843

    Google Scholar 

  51. Yan Z, Shun GS, Ren Q, Lin Z (2007) Palmprint image quality measures in minutiae-based recognition. In: 2007 IWSSIP and EC-SIPMCS-Proc. 2007 14th int. workshop on systems, signals and image processing, and 6th EURASIP conf. focused on speech and image processing, multimedia communications and services, pp 140–143

  52. Yan J, Dai X, Zhao Q, Liu F (2017) A CNN-based fingerprint image quality assessment method. In: Zhou J, Wang Y, Sun Z, Xu Y, Shen L, Feng J, Shan S, Qiao Y, Guo Z, Yu S (eds) Biometric recognition-12th Chinese conference, CCBR 2017, Shenzhen, China, October 28-29, 2017, proceedings, lecture notes in computer science, vol 10568. Springer, pp 344–352

  53. Yang L, Yang G, Yin Y, Xiao R (2013) Finger vein image quality evaluation using support vector machines. Opt Eng 2:027003–1-027003-9

    Google Scholar 

  54. Yang SJ, Berndl M, Ando DM, Barch M, Narayanaswamy A, Christiansen E, Hoyer S, Roat C, Hung J, Rueden CT, Shankar A, Finkbeiner S, Nelson P (2018) Assessing microscope image focus quality with deep learning. BMC Bioinform 19(1):77,1–77,9

    Google Scholar 

  55. Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122

  56. Yue F, Zuo WM, Zhang DP (2010) Survey of palmprint recognition algorithms. Zidonghua Xuebao/ Acta Automatica Sinica 36(3):353–365. Biometric technology;Feature extraction and matching;Feature matching;Palm-print image;Pre-processing method;Recognition algorithm;Research interests;Security application;

    Google Scholar 

  57. Zago GT, Andreão RV, Dorizzi B, Salles EOT (2018) Retinal image quality assessment using deep learning. Comp Bio Med 103:64–70

    Google Scholar 

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Acknowledgements

The authors thank Professor Miguel A. Ferrer and his research group since they made the LPIDB v1.0 database available and patiently helped us solve problems encountered when doing the experiment with Minutia cylinder-code (MCC) [5, 6, 12, 13, 40]. We are grateful to Professor Feng Jianjiang for making the THU-HRPD database available; all of the palmprint images referred to in this paper are from this database. We appreciate Professor Yin Yilong and other members in data Mining, machine Learning, and their Applications (MLA) lab of Shandong University for giving important comments on this work. We also appreciate Professor Zhongli Wei, XinHua Lv and other members of the Evidence Forensic Laboratory in Universities of Shandong Province in Shandong University of Political Science and Law for comments on forensic palmprints.

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, People’s Republic of China

    Hao Fanchang, Li Chenglong & Xia Chuanliang

  2. School of Cybersecurity and the Evidence Forensic Laboratory in Universities of Shandong Province, Shandong University of Political Science and Law, Jinan, 250014, People’s Republic of China

    Chang Xu

  3. School of Software, Shandong University, Jinan, 250101, People’s Republic of China

    Yang Gongping

  4. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, 250014, People’s Republic of China

    Yang Lu

  5. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, 250101, People’s Republic of China

    Li Chengdong

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  1. Hao Fanchang
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Correspondence to Hao Fanchang.

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This work is supported in part by the National Natural Science Foundation of China under Grant 61503220, 61573219, 61672328 and 61703235. It is also supported by the Taishan Scholar Project of Shandong Province under Grant TSQN201812092, the Key Research and Development Project of Shandong Province under Grant 2018GGX101032, 2019GGX101068, the Projects of Shandong Province Higher Educational Science and Technology Program under Grant J17KB182 and J18KA357, and the Doctoral Fund of Shandong Jianzhu University under Grant XNBS1810 and XNBS1811.

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Fanchang, H., Xu, C., Gongping, Y. et al. Local image quality measurement for multi-scale forensic palmprints. Multimed Tools Appl 79, 12915–12938 (2020). https://doi.org/10.1007/s11042-020-08625-y

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