Skip to main content
SpringerLink
Log in

Image-based facial pore detection and visualization in skin health evaluation

  • Regular Paper
  • Published:
Journal of Visualization Aims and scope Submit manuscript

Abstract

The role of health information visualization and visual analytics processes is gaining importance. The facial pore feature is one of the crucial indicators of skin health evaluation. However, pores are tiny, which is difficult to detect and analyze based on the digital picture. In this paper, we aim to visualize facial pores by fabricating data to display their different roughness levels. First, we presented an image-based facial pore detection algorithm that combines the characteristics of skin pigment distribution and optimal scale. Second, based on the pore detection result, we proposed a P-DBSCAN (Pore density-based spatial clustering of applications with noise) algorithm that integrates pore characteristics. Because the local saliences of pores and interferences are different at the biological perspective, the interferences can be considered as noisy data in the scheme of well-known DBSCAN algorithm. As a result, the proposed algorithm determines two essential thresholds for facial pore detection and visualization, and makes it possible to improve the detection accuracy and accomplish visualization. On that basis, an index to objectively evaluate the roughness of skin pores was established by using the optimal scales in SIFT. The experiment results suggest improved accuracy of pore detection, and the facial pore visualization presents pore information directly and efficiently.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Arlia D, Coppola M (2001) Experiments in parallel clustering with dbscan. In: European Conference on Parallel Processing. Springer, pp 326–331

  • Bay H, Ess A, Tuytelaars T, Gool LV (2008) Speeded-up robust features (surf). Comput Vis Image Underst 110(3):346–359

    Article  Google Scholar 

  • Bi C, Yang L, Duan Y, Shi Y (2019) A survey on visualization of tensor field. J Vis 22:1–20

    Article  Google Scholar 

  • Bi C, Yuan Y, Zhang J, Shi Y, Xiang Y, Wang Y, Zhang R (2018) Dynamic mode decomposition based video shot detection. IEEE Access 6:21397–21407

    Article  Google Scholar 

  • Birant D, Kut A (2007) St-dbscan: an algorithm for clustering spatial-temporal data. Data Knowl Eng 60(1):208–221

    Article  Google Scholar 

  • Borah B, Bhattacharyya D (2004) An improved sampling-based dbscan for large spatial databases. In: Proceedings of international conference on intelligent sensing and information processing. IEEE, pp 92–96

  • Cui J, Wu Y, Wang Y, Zheng H, Xu G, Zhang X (2012) A facile and efficient approach for pore-opening detection of anodic aluminum oxide membranes. Appl Surf Sci 258(14):5305–5311

    Article  Google Scholar 

  • Flament F, Francois G, Qiu H, Ye C, Hanaya T, Batisse D, Cointereau-Chardon S, Seixas MDG, Dal Belo SE, Bazin R (2015) Facial skin pores: a multiethnic study. Clin Cosmet Investig Dermatol 8:85

    Article  Google Scholar 

  • Francois G, Maudet A, McDaniel D (2009) Quantification of facial pores using image analysis. Cosmet Dermatol 22(9):457–465

    Google Scholar 

  • Hiraoka M, Firbank M, Essenpreis M, Cope M, Arridge S, Van Der Zee P, Delpy D (1993) A monte carlo investigation of optical pathlength in inhomogeneous tissue and its application to near-infrared spectroscopy. Phys Med Biol 38(12):1859–1876

    Article  Google Scholar 

  • Tamura H, Mori S, Yamawaki T (1978) Textural features corresponding to visual perception. IEEE Trans Syst Man Cybern 8(6):460–473

    Article  Google Scholar 

  • Igarashi T, Nishino K, Nayar SK (2007) The appearance of human skin: a survey. Comput Graph Vis 3(1):1–95

    Google Scholar 

  • Jang SI, Kim EJ, Lee HK (2018) A method of evaluating facial pores using optical 2D images and analysis of age-dependent changes in facial pores in koreans. Skin Res Technol 24(2):304–308

    Article  Google Scholar 

  • Jolivot R, Benezeth Y, Marzani F (2013) Skin parameter map retrieval from a dedicated multispectral imaging system applied to dermatology/cosmetology. J Biomed Imaging 2013(26):1–15

    Google Scholar 

  • Joswig JO, Lorenz T (2016) Detecting and quantifying geometric features in large series of cluster structures. Zeitschrift für Physikalische Chemie 230:1057–1066

    Google Scholar 

  • Jung HJ, Ahn JY, Lee JI, Bae JY, Kim HL, Suh HY, Park MY (2018) Analysis of the number of enlarged pores according to site, age, and sex. Skin Res Technol 24(3):367–370

    Article  Google Scholar 

  • Kakudo N, Kushida S, Tanaka N, Minakata T, Suzuki K, Kusumoto K (2011) A novel method to measure conspicuous facial pores using computer analysis of digital-camera-captured images: the effect of glycolic acid chemical peeling. Skin Res Technol 17(4):427–433

    Article  Google Scholar 

  • Khan NY, McCane B, Wyvill G (2011) Sift and surf performance evaluation against various image deformations on benchmark dataset. In: 2011 International conference on digital image computing: techniques and applications. IEEE, pp 501–506

  • Kharkar VP, Ratnaparkhe VR (2013) Hemoglobin estimation methods: a review of clinical, sensor and image processing methods. Int J Eng Res Technol 2(1):1–7

    Article  Google Scholar 

  • Li D, Lam KM (2015) Design and learn distinctive features from pore-scale facial keypoints. Pattern Recogn 48(3):732–745

    Article  Google Scholar 

  • Liu Z, Zerubia J (2013) Melanin and hemoglobin identification for skin disease analysis. In: 2nd IAPR Asian conference on pattern recognition, IEEE

  • Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  • Malathi S, Meena C (2011) Improved partial fingerprint matching based on score level fusion using pore and sift features. In: International conference on process automation, control and computing, pp 1–4

  • McGrath JA, Eady RAJ, Pope FM (2004) Anatomy and organization of human skin. Rook’s textbook of dermatology. Blackwell Publishing, Oxford

    Google Scholar 

  • Ning Y, Zeng Q, Wang Q, Li L (2017) Evaluating photographic scales of facial pores and diagnostic agreement of tests using latent class models. J Cosmet Laser Ther 19(1):64–67

    Article  Google Scholar 

  • Savran A, Alyüz N, Dibeklioğlu H, Çeliktutan O, Gökberk B, Sankur B, Akarun L (2008) Bosphorus database for 3d face analysis. In: European workshop on biometrics and identity management. Springer, pp 47–56

  • Savran A, Sankur B, Bilge MT (2012) Comparative evaluation of 3D versus 2D modality for automatic detection of facial action units. Pattern Recogn 45(2):767–782

    Article  Google Scholar 

  • Savran A, Sankur B, Bilge MT (2012) Regression-based intensity estimation of facial action units. Image Vis Comput 30(10):774–784

    Article  Google Scholar 

  • Shaiek A, Flament F, François G, Descamps VL, Barla C, Vicic M, Giron F, Bazin R (2017) A new tool to quantify the geometrical characteristics of facial skin pores. Changes with age and a making-up procedure in caucasian women. Skin Res Technol 23(2):249–257

    Article  Google Scholar 

  • Sun JY, Kim SW, Lee SH, Choi JE, Ko S (2017) Automatic facial pore analysis system using multi-scale pore detection. Skin Res Technol 23(3):354–362

    Article  Google Scholar 

  • Tran TN, Drab K, Daszykowski M (2013) Revised dbscan algorithm to cluster data with dense adjacent clusters. Chemometr Intell Lab Syst 120:92–96

    Article  Google Scholar 

  • Tsumura N, Haneishi H, Miyake Y (1999) Independent-component analysis of skin color image. Journal of the Optical Society of America A Optics Image Science & Vision 16(9):2169–2176

    Article  Google Scholar 

  • Tsumura N, Ojima N, Sato K, Shiraishi M (2003) Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin. ACM Trans Graph 22(3):770–779

    Article  Google Scholar 

  • Villegas C, Climent J, Villegas C (2014) Using skin melanin layer for facial pore identification in rgb digital images. Int J Emerg Technol Adv Eng 4(8):335–342

    Google Scholar 

  • Wang X, Liang Y, Zeng X, Li D, Jia W (2017) Deeply learned pore-scale facial features. In: Chinese conference on biometric recognition, pp 135–144

    Chapter  Google Scholar 

  • Xu S, Zhang S, Zhang Y (2011) Robust algorithm for extracting skin pigment concentration from color image. J Zhejiang Univ 2:253–258

    Google Scholar 

  • Yang L, Wang B, Zhang R, Zhou H, Wang R (2018) Analysis on location accuracy for the binocular stereo vision system. IEEE Photonics J 10(1):1–16

    Google Scholar 

  • Zeng X, Li YZD, Lam KM (2017) Pore-scale facial features matching under 3D morphable model constraint. In: CCF Chinese conference on computer vision

  • Zhao Y, Luo F, Chen M, Wang Y, Xia J, Zhou F, Wang Y, Chen Y, Chen W (2019) Evaluating multi-dimensional visualizations for understanding fuzzy clusters. IEEE Trans Vis Comput Graph 25(1):12–21

    Article  Google Scholar 

  • Ziel R, Haus A, Tulke A (2008) Quantification of the pore size distribution (porosity profiles) in microfiltration membranes by sem, tem and computer image analysis. J Membr Sci 323(2):241–246

    Article  Google Scholar 

Download references

Acknowledgements

This work was presented in the paper is partially supported by grants from the Natural Science Foundation of Tianjin (No.16JCYBJC42000, No.18JCYBJC85100), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No.19YJA630046). The statements made herein are solely the responsibility of the authors.

Author information

Authors and Affiliations

  1. Department of Information Science and Technology, Tianjin University of Finance and Economics, Tianjin, China

    Zhen Wang & Ruoxuan Li

  2. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Chongke Bi

Authors
  1. Zhen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ruoxuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chongke Bi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhen Wang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Li, R. & Bi, C. Image-based facial pore detection and visualization in skin health evaluation. J Vis 22, 1039–1055 (2019). https://doi.org/10.1007/s12650-019-00581-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12650-019-00581-6

Keywords

Search

Navigation