Automatic, Objective, and Efficient Measurement of Pain Using Automated Face Analysis

  • Zakia Hammal
  • Jeffrey F. CohnEmail author


Pain typically is measured by patient self-report, but self-reported pain is difficult to interpret and may be impaired or in some circumstances not possible to obtain. Automatic, objective assessment of pain from video or camera input is emerging as a powerful alternative. We review the current state of the art in automatic, objective assessment of pain from video or camera input and the databases that have made progress in this area possible. Because most efforts have involved facial expression of pain, we emphasize that in our review. We discuss current challenges and prospects to advance automatic assessment of the occurrence and intensity of pain for research and clinical use.


Automatic Objective measurement of pain Facial expression Pain intensity 



This work was supported by the National Institute of Nursing Research of the National Institutes of Health under Award Number R21NR016510. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


  1. Ahmed, N., Natarajan, T., & Rao, K. R. (1974). Discrete cosine transform. IEEE Transactions on Computers, 23, 90–93.CrossRefGoogle Scholar
  2. Arif, M., Grap, M. J., Munro, C. L., Lyon, D. E., Sessler, D. C. N., & Cohn, J. F. (2010). Facial expression and pain in the critically-ill, non-communicative patient: State of science review. Intensive and Critical Care Medicine, 26, 343–352.CrossRefGoogle Scholar
  3. Ashraf, A. B., Lucey, S., Cohn, J. F., Chen, T., Prkachin, K. M., & Solomon, P. E. (2009). The painful face: Pain expression recognition using active appearance models. Image and Vision Computing, 27, 1788–1796.CrossRefGoogle Scholar
  4. Aung, M. S. H., Kaltwang, S., Romera-Paredes, B., Martinez, B., Singh, A., Cella, M., … Bianchi-Berthouze, N. (2015). The automatic detection of chronic pain-related expression: Requirements, challenges and a multimodal dataset. IEEE Transactions on Affective Computing, 7, 435–451.CrossRefGoogle Scholar
  5. Baltrusaitis, T., Robinson, P., & Morency, L. P. (2012). 3D constrained local model for rigid and non-rigid facial tracking. In IEEE CVPR. Google Scholar
  6. Bartlett, M., Littlewort, G., Frank, M., & Lee, K. (2014). Automated detection of deceptive facial expressions of Pain. Current Biology, 24(7), 738–743.CrossRefGoogle Scholar
  7. Brennan, R. L., & Prediger, D. J. (1981). Coefficient kappa: Some uses, misuses, and alternatives. Educational and Psychological Measurement, 41, 687–699.CrossRefGoogle Scholar
  8. Brummer, N., & du Preez, J. (2005). Application-independent evaluation of speaker detection. Computer Speech and Language, 20, 230–275.CrossRefGoogle Scholar
  9. Chambers, C. T., Reid, G. J., Craig, K. D., McGrath, P. J., & Finley, G. A. (1998). Agreement between child and parent reports of pain. The Clinical Journal of Pain, 14, 336–342.CrossRefGoogle Scholar
  10. Chew, S. W., Lucey, P., Lucey, S., Saragih, J. M., Cohn, J. F., Matthews, I., & Sridharan, S. (2012). In the pursuit of effective affective computing: The relationship between features and registration. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 42(4), 1–12.CrossRefGoogle Scholar
  11. Clemente, C. D. (1997). Anatomy: A regional atlas of the human body (4th ed.). Baltimore, MD: Williams & Wilkins.Google Scholar
  12. Cohn, J. F., Ambadar, Z., & Ekman, P. (2007). Observer-based measurement of facial expression with the facial action coding system. In J. A. Coan & J. J. B. Allen (Eds.), Handbook of emotion elicitation and assessment, Oxford University Press series in affective science (pp. 203–221). New York, NY: Oxford University Press.Google Scholar
  13. Coll, M. P., Gregoire, M., Latimer, M., Eugene, F., & Jackson, P. L. (2011). Perception of pain in others: Implications for caregivers. Pain Management, 1(3), 257–265.CrossRefGoogle Scholar
  14. Cootes, T., Edwards, G., & Taylor, C. (2001). Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6), 681–685.CrossRefGoogle Scholar
  15. Corneanu, C., Oliu, M., Cohn, J. F., & Escalera, S. (2015). Survey on RGB, thermal, and multimodal approaches for facial expression analysis: History, trends, and affect-related applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 1548–1568.CrossRefGoogle Scholar
  16. Craig, K. D. (2009). A social communications model of pain. Canadian Psychology/Psychologie canadienne, 50, 22–32. doi:10.1037/a0014772CrossRefGoogle Scholar
  17. Craig, K. D., Hyde, S. A., & Patrick, C. J. (1991). Genuine, suppressed and faked facial behavior during exacerbation of chronic low back pain. Pain, 46(2), 161–171.CrossRefGoogle Scholar
  18. Craig, K. D., Korol, C. T., & Pillai, R. R. (2002). Challenges of judging pain in vulnerable infants. Clinics in Perinatology, 29, 445–457.CrossRefGoogle Scholar
  19. Craig, K. D., Prkachin, K. M., & Grunau, R. V. E. (2001). The facial expression of pain. In D. C. Turk & R. Melzack (Eds.), Handbook of pain assessment (2nd ed.). New York, NY: Guilford Press.Google Scholar
  20. Craig, K. D., Prkachin, K. M., & Grunau, R. V. E. (2010). The facial expression of pain. In D. C. Turk & R. Melzack (Eds.), Handbook of pain assessment (3rd ed.). New York, NY: Guilford Press.Google Scholar
  21. Craig, K. D., Versloot, J., Goubert, L., Vervoort, T., & Crombez, G. (2010). Perceiving pain in others: Automatic and controlled mechanisms. The Journal of Pain, 11(8), 101–108.CrossRefGoogle Scholar
  22. Cummins, N., Epps, J., & Ambikairajah, E. (2013). Spectro temporal analysis of speech affected by depression and psychomotor retardation. In IEEE ICASSP (pp. 7542–7546).Google Scholar
  23. Darwin, C. (1872/1998). The expression of the emotions in man and animals (3rd ed.). New York, NY: Oxford University Press.CrossRefGoogle Scholar
  24. de Knegt, N. C., Pieper, M. J., Lobbezoo, F., Schuengel, C., Evenhuis, H. M., Passchier, J., & Scherder, E. J. (2013). Behavioral pain indicators in people with intellectual disabilities: A systematic review. Journal of Pain, 14(9), 885–896.CrossRefGoogle Scholar
  25. Dubois, A., Bringuier, S., Capdevilla, X., & Pry, R. (2008). Vocal and verbal expression of postoperative pain in preschoolers. Pain Management Nursing, 9(4), 160–165.CrossRefGoogle Scholar
  26. Ekman, P., & Friesen, W. V. (1978). Facial action coding system. Palo Alto, CA: Consulting Psychologists Press.Google Scholar
  27. Ekman, P., Friesen, W. V., & Hager, J. C. (2002). Facial action coding system. Salt Lake City, UT: Research Nexus, Network Research Information.Google Scholar
  28. El Ayadi, M., Kamel, M. S., & Karray, F. (2011). Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition, 44(3), 572–587.CrossRefGoogle Scholar
  29. Fillingim, R. B., King, C. D., Ribeiro-Dasilva, M. C., Rahim-Williams, B., & Riley, J. L. (2009). Sex, gender, and pain: A review of recent clinical and experimental findings. The Journal of Pain, 10(5), 447–485.CrossRefGoogle Scholar
  30. Florea, C., Florea, L., & Vertan, C. (2014). Learning pain from emotion: Transferred HoT data representation for pain intensity estimation. In ECCV Workshop on ACVR, Zurich, Switzerland.Google Scholar
  31. Garrett, K. L., Happ, M. B., Costello, J. R., & Fried-Oken, M. B. (2007). AAC in the intensive care unit. In D. R. Beukelman, K. L. Garrett, & K. M. Yorkston (Eds.), Augmentative communication strategies for adults with acute or chronic medical conditions. Baltimore, MD: Paul H. Brookes.Google Scholar
  32. Gélinas, C., Arbour, C., Michaud, C., Vaillant, F., & Desjardins, S. (2011). Implementation of the critical-care pain observation tool on pain assessment/management nursing practices in an intensive care unit with nonverbal critically ill adults: A before and after study. International Journal of Nursing Studies, 48(12), 1495–1504.CrossRefGoogle Scholar
  33. Ghosh, S., Laksana, E., Scherer, S. & Morency, L.-P. (2015) A multi-label convolutional neural network approach to cross-domain action unit detection, presented at the Affective Computing and Intelligent Interaction, Xi’an, China, 2015.Google Scholar
  34. Girard, J. M., & Cohn, J. F. (2016). A primer on observational measurement. Assessment, 23(4), 404–413.CrossRefGoogle Scholar
  35. Green, C. R., Anderson, K. O., Baker, T. A., Campbell, L. C., Decker, S., Fillingim, R. B., … Vallerand, A. H. (2003). The unequal burden of pain: Confronting racial and ethnic disparities in pain. Pain Medicine, 4(3), 277–294.CrossRefGoogle Scholar
  36. Hadjistavropoulos, T., Craig, K. D., Duck, S., Cano, A., Goubert, L., Jackson, P. L., … Fitzgerald, T. D. (2011). A biopsychosocial formulation of pain communication. Psychological Bulletin, 137(6), 910–939.CrossRefGoogle Scholar
  37. Hammal Z., & Cohn J. F. (2012, October 23–25). Automatic detection of pain intensity. In Proc. 14th ICMI, 47–52, Santa Monica, CA.Google Scholar
  38. Hammal, Z., & Cohn, J. F. (2014, November 12–16). Intra- and interpersonal functions of head motion in emotion communication. In RFMI in Conjunction with the 16th ACM International Conference on Multimodal Interaction ICMI 2014, Istanbul, Turkey.Google Scholar
  39. Hammal, Z., Cohn, J. F., & George, D. T. (2014). Interpersonal coordination of head motion in distressed couples. IEEE Transactions on Affective Computing, 5(2), 155–167.CrossRefGoogle Scholar
  40. Hammal, Z., Cohn, J. F., Heike, C., & Speltz, M. L. (2015, September 21–24). What can head and facial movements convey about positive and negative affect? In The 6th Biannual Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII 2015), Xi’an, China (Best Paper Award).Google Scholar
  41. Hammal, Z., Cohn, J. F., & Messinger, D. (2015). Head movement dynamics during normal and perturbed mother-infant interaction. IEEE Transactions on Affective Computing, 6(4), 361–370.CrossRefGoogle Scholar
  42. Hammal, Z., & Kunz, M. (2012). Pain monitoring: A dynamic and context-sensitive system. Pattern Recognition, 45(4), 1265–1280.CrossRefGoogle Scholar
  43. Hammal, Z., Kunz, M., Arguin, M., & Gosselin, F. (2008, September 22–24). Spontaneous pain expression recognition in video sequences. In Proc. BCS Int’l Conf. on Visions of Computer Science (BCS-Visions 2008), Imperial College, London, England.Google Scholar
  44. Hammal, Z., & Massot, C. (2011). Gabor-like image filtering for transient feature detection and global energy estimation applied to multi-expression classification. In P. Richard & J. Braz (Eds.), Communications in computer and information science (CCIS 229) (pp. 135–153). Heidelberg, Germany: Springer.Google Scholar
  45. Haugstad, G. K., Haugstad, T. S., Kirste, U. M., Leganger, S., Wojniusz, S., Klemmetsen, I., & Malt, U. F. (2006). Posture, movement patterns, and body awareness in women with chronic pelvic pain. Journal of Psychosomatic Research, 61(5), 637–644.CrossRefGoogle Scholar
  46. Ho, T. K. (1995, August 14–16). Random decision forests (PDF). In Proceedings of the 3rd International Conference on Document Analysis and Recognition (pp. 278–282), Montreal, QC.Google Scholar
  47. Ho, T. K. (1998). The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.CrossRefGoogle Scholar
  48. Hofle, M., Hauck, M., Engel, A. K., & Senkowski, D. (2012). Viewing a needle pricking a hand that you perceive as yours enhances unpleasantness of pain. Pain, 153(3), 1074–1081.CrossRefGoogle Scholar
  49. Jaiswal, S., & Valstar, M. F. (2016) Deep learning the dynamic appearance and shape of facial action units, presented at the Winter Conference on Applications of Computer Vision (WACV), Lake Placid, USA, 2016.Google Scholar
  50. Jeni, L. A., Cohn, J. F., & Kanade, T. (2016). Dense 3D face alignment from 2d video for real-time use. Image Vision and Computing, 58, 13–24.CrossRefGoogle Scholar
  51. Joshi, J., Dhall, A., Goecke, R., & Cohn, J. (2013, September 2–5). Relative body part movement for automatic depression analysis. In Proc. 5th ACII, Geneva, Switzerland.Google Scholar
  52. Kachele, M., Thiam, P., Amirian, M., Werner, P., Walter, S., Schwenker, F., & Palm, G. (2015). Multimodal data fusion for person-independent, continuous estimation of pain intensity. In L. Iliadis & C. Jayne (Eds.), Engineering applications of neural networks, Communications in computer and information science (Vol. 517, pp. 275–285). Berlin, Germany: Springer.CrossRefGoogle Scholar
  53. Kaltwang, S., Rudovic, O., & Pantic, M. (2012). Continuous pain intensity estimation from facial expressions. In G. Bebis et al. (Eds.), Proceedings of the 8th International Symposium on Advances in Visual Computing, ISVC 2012, LNCS (Vol. 7432, pp. 368–377). Heidelberg, Germany: Springer.Google Scholar
  54. Karg, M., Samadani, A. A., Gorbert, R., Kuhnlenz, K., Hoey, J., & Kulic, D. (2014). Body movements for affective expression: A survey of automatic recognition and generation. IEEE Transactions on Affective Computing, 4(4), 341–359.CrossRefGoogle Scholar
  55. Kleinsmith, A., & Bianchi-Berthouze, N. (2013). Affective body expression perception and recognition: A survey. IEEE Transactions on Affective Computing, 4(1), 15–33.CrossRefGoogle Scholar
  56. Kunz, M., Chatelle, C., Lautenbacher, S., & Rainville, P. (2008). The relation between catastrophizing and facial responsiveness to pain. Pain, 140, 127–134.CrossRefGoogle Scholar
  57. Kvale, A., Ljunggren, A. E., & Johnsen, T. B. (2003). Examination of movement in patients with long-lasting musculoskeletal pain: Reliability and validity. Physiotherapy Research International, 8, 36–52.CrossRefGoogle Scholar
  58. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.CrossRefGoogle Scholar
  59. Littlewort, G., Bartlett, M., & Lee, K. (2009). Automatic coding of facial expressions displayed during posed and genuine pain. Image and Vision Computing, 27(12), 1741–1844.CrossRefGoogle Scholar
  60. Lucey, P., Cohn, J. F., Matthews, I., Lucey, S., Sridharan, S., Howlett, J., & Prkachin, K. M. (2011). Automatically detecting pain in video through facial action unit recognition. Systems, Man, and Cybernetics, Part B, 41(3), 664–674.CrossRefGoogle Scholar
  61. Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P., Chew, S., & Matthews, I. (2012). Painful monitoring: Automatic pain monitoring using the UNBC-McMaster shoulder pain expression archive database. Image and Vision Computing, 30(3), 197–205.CrossRefGoogle Scholar
  62. Lucey, P., Cohn, J. F., Prkachin, K. M., Solomon, P., & Matthews, I. (2011). Painful data: The UNBC-McMaster shoulder pain expression archive database. In IEEE International Conference on Automatic Face and Gesture Recognition (FG2011), Santa Barbara, CA.Google Scholar
  63. Monroe, T. B., & Mion, L. C. (2012). Patients with advanced dementia: How do we know if they are in pain? Geriatric Nursing, 33(3), 226–228.CrossRefGoogle Scholar
  64. Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on feature distributions. Pattern Recognition, 29(1), 51–59.CrossRefGoogle Scholar
  65. Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 971–987.CrossRefGoogle Scholar
  66. Ojansivu, V., & Heikkila, J.. (2008). Blur insensitive texture classification using local phase quantization. In Proceedings on international conference on image and signal processing (pp. 236–243).Google Scholar
  67. Payen, J. F., Bru, O., Bosson, J. L., Lagrasta, A., Novel, E., Deschaux, I., … Jacquot, C. (2001). Assessing pain in critically ill sedated patients by using a behavioral pain scale. Critical Care Medicine, 29(12), 2258–2263.CrossRefGoogle Scholar
  68. Peters, M. L., & Vancleef, L. M. G. (2008). The role of personality traits in pain perception and disability. Reviews in Analgesia, 10, 11–21.CrossRefGoogle Scholar
  69. Pietikäinen, M. (2010). Local binary patterns. Scholarpedia, 5(3), 9775.CrossRefGoogle Scholar
  70. Prkachin, K. M. (1992). The consistency of facial expressions of pain: A comparison across modalities. Pain, 51, 297–306.CrossRefGoogle Scholar
  71. Prkachin, K. M., Solomon, P., Hwang, T., & Mercer, S. R. (2001). Does experience influences judgments of pain behaviour? Evidence from relatives of pain patients and therapists. Pain Research & Management, 6, 105–112.CrossRefGoogle Scholar
  72. Prkachin, K. M., & Solomon, P. E. (2008). The structure, reliability and validity of pain expression: Evidence from patients with shoulder pain. Pain, 139, 267–274.CrossRefGoogle Scholar
  73. Rajasagaram, U., Taylor, D. M., Braitberg, G., Pearsell, J. P., & Capp, B. A. (2009). Paediatric pain assessment: Differences between triage nurse, child and parent. Journal of Paediatrics and Child Health, 45(4), 199–203.CrossRefGoogle Scholar
  74. Rash, J. A., Prkachin, K. M., Solomon, P. E., & Campbell, T. A. (n.d.). Assessing the efficacy of a manual-based intervention for improving the detection of facial pain expression: The index of facial pain expression (Unpublished manuscript).Google Scholar
  75. Rudovic, O., Pavlovic, V., & Pantic, M. (2013). Automatic pain intensity estimation with heteroscedastic conditional ordinal random fields. In Proceedings of the 9th Int’l Symposium on Advances in Visual Computing, ISVC, Part II, Greece, LNCS (Vol. 8034, pp. 234–243). Heidelberg, Germany: Springer.Google Scholar
  76. Saragih, J., Lucey, S., & Cohn, J. F. (2011). Deformable model fitting by regularized landmark mean shift. International Journal of Computer Vision, 91(2), 200–215.CrossRefGoogle Scholar
  77. Scherer, K. R. (2003). Vocal communication of emotion: A review of research paradigms. Speech Communication, 40, 227–256.CrossRefGoogle Scholar
  78. Schuller, B., Batliner, A., Steidl, S., & Seppi, D. (2011). Recognizing realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge. Speech Communication, 53(9/10), 1062–1087. Special Issue: Sensing Emotion and Affect – Facing Realism in Speech Processing.CrossRefGoogle Scholar
  79. Shrout, P. E., & Fleiss, J. L. (1979). Intraclass correlations: Uses in assessing rater reliability. Psychological Bulletin, 86, 420–428.CrossRefGoogle Scholar
  80. Sikka, K., Ahmed, A., Diaz, D., Goodwin, M., Craig, K., Bartlett, M., & Huang, J. (2015). Automated assessment of children’s post-operative pain using computer vision. Pediatrics, 136, 124–131.CrossRefGoogle Scholar
  81. Sikka, K., Dhall, A., & Bartlett, M. (2014). Weakly supervised pain localization and classification with multiple segment learning. Image and Vision Computing, 32(10), 659–670.CrossRefGoogle Scholar
  82. Singer, A. J., Gulla, J., & Thode, H. C., Jr. (2002). Parents and practitioners are poor judges of young children’s pain severity. Academic Emergency Medicine, 9(6), 609–612.CrossRefGoogle Scholar
  83. Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. The Journal of Machine Learning Research, 1, 211–244.Google Scholar
  84. Vapnik, V. (1995). The nature of statistical learning theory. New York, NY: Springer-Verlag.CrossRefGoogle Scholar
  85. Vapnik, V. (1998). Statistical learning theory. New York, NY: John Wiley & Sons.Google Scholar
  86. Vlaeyen, J. W. S., & Linton, S. J. (2000). Fear-avoidance and its consequences in muscle skeleton pain: A state of the art. Pain, 85(3), 317–332.CrossRefGoogle Scholar
  87. Walter, S., Gruss, S., Ehleiter, H., Tan, J., Traue, H., Werner, P., … Moreira da Silva, G. (2013) The BioVid Heat Pain Database: Data for the advancement and systematic validation of an automated pain recognition system. In 2013 Proceedings of IEEE International Conference on Cybernetics.Google Scholar
  88. Wandner, L. D., Scipio, C. D., Hirsh, A. T., Torres, C. A., & Robinson, M. E. (2012). The perception of pain in others: How gender, race, and age influence pain expectations. The Journal of Pain, 13(3), 220–227.CrossRefGoogle Scholar
  89. Warden, V., Hurley, A. C., & Volicer, L. (2003). Development and psychometric evaluation of the pain assessment in advanced dementia scale. Journal of the American Medical Directors Association, 4(1), 9–15.CrossRefGoogle Scholar
  90. Werner, P., Al-Hamadi, A., Limbrecht-Ecklundt, K., Walter, S., Gruss, S., & Traue, H. (2016). Automatic pain assessment with facial activity descriptors. IEEE Transactions on Affective Computing, 8, 286–299.CrossRefGoogle Scholar
  91. Williamson, J. R., Quatieri, T. F., Helfer, B. S., Horwitz, R., Daryush, B. Y., & Mehta, D. (2013). Vocal biomarkers of depression based on motor incoordination. In Proc. ACM AVEC (pp. 41–48).Google Scholar
  92. Yang, Y., Fairbairn, C., & Cohn, J. F. (2013). Detecting depression severity from vocal prosody. IEEE Transactions on Affective Computing, 4(2), 142–150.CrossRefGoogle Scholar
  93. Yang, R., Tong, S., López, M. B., Boutellaa, E., Peng, J., Feng, X., & Hadid, A. (2016, December). On pain assessment from facial videos using spatio-temporal local descriptors. In IPTA (pp. 1–6).Google Scholar
  94. Zagyapan, R., Iyem, C., Kurkcuoglu, A., Pelin, C., & Tekindal, M. A. (2012). The relationship between balance, muscles, and anthropomorphic features in young adults. Cairo, Egypt: Hindawi Publishing Corporation, Anatomy Research International.Google Scholar
  95. Zhao, G., & Pietikäinen, M. (2007). Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6), 915–928.CrossRefGoogle Scholar
  96. Zhang, X., & De la Torre, F. (2015). Global supervised descent method. In Proceedings of the IEEE International Conference on Computer Vision. Google Scholar
  97. Zhou, H., Roberts, P., & Horgan, L. (2008). Association between self-report pain ratings of child and parent, child and nurse and parent and nurse dyads: Meta-analysis. Journal of Advanced Nursing, 63(4), 334–342.CrossRefGoogle Scholar
  98. Zhou, J., Hong, X., Su, F., & Zhao, G. (2016). Recurrent convolutional neural network regression for continuous pain intensity estimation in video. In IEEE CVPR Workshop of Affect “in-the-Wild” (pp. 84–92).Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.University of PittsburghPittsburghUSA

Personalised recommendations