Skip to main content
SpringerLink
Log in

How Brow Rotation Affects Emotional Expression Utilizing Artificial Intelligence

  • Original Articles
  • Craniofacial/Maxillofacial
  • Published:
Aesthetic Plastic Surgery Aims and scope Submit manuscript

Abstract

Background

It is well known that brow position affects emotional expression. However, there is little literature on how and to what degree this change in emotional expression happens. Previous studies on this topic have utilized manual rating; this method of study remains small and labor intensive. Our objective is to correlate manual brow rotations with emotional outcomes using artificial intelligence to objectively determine how specific brow manipulations affected human expression.

Methods

We included 53 brow-lift patients in this study. Pre-operative patients’ brows were rotated to − 20, − 10, +10, and +20 degrees in respect to the central axis of their existing brow using PIXLR, a cloud-based set of image editing tools and utilities. These images were analyzed using FaceReader, a validated software package that uses computer vision technology for facial expression recognition. The primary facial emotion and intensity of facial action units (0 = no action unit detected to 4 = most intense action unit detected) generated by the software were recorded.

Results

265 total images [5 images (pre-operative, − 20 degree brow rotation, − 10, +10, and +20) per patient] were analyzed using FaceReader. The primary emotion detected in the majority of images was neutral. The percentage of disgust in patients’ expressions, as detected by FaceReader, increased with increased positive brow rotation (1.76% disgust detected at − 20 degrees, 2.09% at − 10 degrees, 2.65% at neutral, 2.61% at +10 degrees, and 2.95% at +20 degrees). In contrast, the percentage of sadness in patients’ expressions decreased with increased positive brow rotation (29.92% sadness detected at − 20 degrees, 21.5% at − 10 degrees, 11.42% at neutral, 15.75% at +10 degrees, and 12.86% at +20 degrees).

Our facial action unit analysis corresponded with primary emotion analysis. The intensity of the inner brow raiser decreased with increased positive brow rotation 8.54% at − 20 degrees, 4.21% at − 10 degrees, 1.48% at neutral, 0.84% at +10 degrees, and 0.76% at +20 degrees). The intensity of the outer brow raiser increased with increased positive brow rotation (0.97% at − 20 degrees, 0.45% at − 10 degrees, 1.12% at neutral, 5.45% at +10 degrees, and 11.19% at +20 degrees).

Conclusion

We demonstrated that increasing the degree of brow rotation correlated positively with the percentage of disgust and inversely with the percentage of sadness detected by FaceReader. This study demonstrated how different manipulated brow positions affected emotional outcomes using artificial intelligence. Physicians can use these findings to better understand how brow-lifts can affect the perceived emotion of their patients.

Level of Evidence III

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.

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

Similar content being viewed by others

References

  1. Berkowitz RL, Jacobs DI, Gorman PJ (2005) Brow fixation with the endotine forehead device in endoscopic brow lift. Plast Reconstr Surg 116(6):1761–1767

    Article  CAS  PubMed  Google Scholar 

  2. Booth AJ, Murray A, Tyers AG (2004) The direct brow lift: efficacy, complications, and patient satisfaction. Br J Ophthalmol 88(5):688–691

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Chajchir A (1994) Endoscopic subperiosteal forehead lift. Aesthetic Plast Surg 18(3):269–274

    Article  CAS  PubMed  Google Scholar 

  4. Pelle-Ceravolo M, Angelini M (2017) Transcutaneous brow shaping: a straightforward and precise method to lift and shape the eyebrows. Aesthetic Surg J 37(8):863–875

    Article  Google Scholar 

  5. Guyuron B (2001) Corrugator supercilii resection through blepharoplasty incision. Plast Reconstr Surg 107(2):604–605

    Article  CAS  PubMed  Google Scholar 

  6. Viterbo F, Auersvald A, O’Daniel TG (2019) Gliding brow lift (GBL): a new concept. Aesthetic Plast Surg 43(6):1536–1546

    Article  PubMed  Google Scholar 

  7. Trepsat F (2003) Periorbital rejuvenation combining fat grafting and blepharoplasties. Aesthetic Plast Surg 27(4):243–253

    Article  PubMed  Google Scholar 

  8. Gunter JP, Antrobus SD (1997) Aesthetic analysis of the eyebrows. Plast Reconstr Surg 99(7):1808–1816

    Article  CAS  PubMed  Google Scholar 

  9. Freund RM, Nolan WB 3rd (1996) Correlation between brow lift outcomes and aesthetic ideals for eyebrow height and shape in females. Plast Reconstr Surg 97(7):1343–1348

    Article  CAS  PubMed  Google Scholar 

  10. Matarasso A, Terino EO (1994) Forehead-brow rhytidoplasty: reassessing the goals. Plast Reconstr Surg 93(7):1378–1389

    Article  CAS  PubMed  Google Scholar 

  11. Schreiber JE, Singh NK, Klatsky SA (2005) Beauty Lies in the “Eyebrow” of the Beholder: A Public Survey of Eyebrow Aesthetics. Aesthetic Surg J 25(4):348–352

    Article  CAS  Google Scholar 

  12. Westmore M (1974) Facial cosmetics in conjunction with surgery. Paper presented at: Aesthetic Plastic Surgical Society Meeting; May 7, 1974; Vancouver, British Columbia.

  13. Knoll BI, Attkiss KJ, Persing JA (2008) The influence of forehead, brow, and periorbital aesthetics on perceived expression in the youthful face. Plast Reconstr Surg 121(5):1793–1802

    Article  CAS  PubMed  Google Scholar 

  14. Baker SB, Dayan JH, Crane A, Kim S (2007) The influence of brow shape on the perception of facial form and brow aesthetics. Plast Reconstr Surg 119(7):2240–2247

    Article  CAS  PubMed  Google Scholar 

  15. Ekman P (1993) Facial expression and emotion. Am Psychol 48(4):384–392

    Article  CAS  PubMed  Google Scholar 

  16. Kirkpatrick SW, Bell FE, Johnson C, Perkins J, Sullivan LA (1996) Interpretation of facial expressions of emotion: the influence of eyebrows. Genet Soc Gen Psychol Monogr 122(4):405–423

    CAS  PubMed  Google Scholar 

  17. Calder AJ, Burton AM, Miller P, Young AW, Akamatsu S (2001) A principal component analysis of facial expressions. Vision Res 41(9):1179–1208

    Article  CAS  PubMed  Google Scholar 

  18. Boonipat T, Lin J, Bite U (2021) Detection of baseline emotion in brow lift patients using artificial intelligence. Aesthetic Plast Surg 45(6):2742–2748

    Article  PubMed  Google Scholar 

  19. van der Schalk J, Hawk ST, Fischer AH, Doosje B (2011) Moving faces, looking places: Validation of the Amsterdam Dynamic Facial Expression Set (ADFES). Emotion 11(4):907–920.

  20. Bishop CM (1995) Neural Networks for Pattern Recognition. Oxford University Press, Inc.

    Book  Google Scholar 

  21. Cootes TF, Edwards GJ, Taylor CJ (2001) Active appearance models. IEEE Trans Pattern Anal Mach Intell 23(6):681–685

    Article  Google Scholar 

  22. van der Schalk J, Hawk ST, Fischer AH, Doosje B (2011) Moving faces, looking places: validation of the amsterdam dynamic facial expression Set (ADFES). Emotion 11(4):907–920

    Article  PubMed  Google Scholar 

  23. Klassen AF, Cano SJ, Schwitzer JA, Scott AM, Pusic AL (2015) FACE-Q scales for health-related quality of life, early life impact, satisfaction with outcomes, and decision to have treatment: development and validation. Plast Reconstr Surg 135(2):375–386

    Article  CAS  PubMed  Google Scholar 

  24. Ching S, Thoma A, McCabe RE, Antony MM (2003) Measuring outcomes in aesthetic surgery: a comprehensive review of the literature. Plast Reconstr Surg 111(1):469–480

    Article  PubMed  Google Scholar 

  25. Boonipat T, Asaad M, Lin J, Glass GE, Mardini S, Stotland M (2020) Using artificial intelligence to measure facial expression following facial reanimation surgery. Plast Reconstr Surg 146(5):1147–1150

    Article  CAS  PubMed  Google Scholar 

  26. Boonipat T, Hebel N, Zhu A, Lin J, Shapiro D (2022) Using artificial intelligence to analyze emotion and facial action units following facial rejuvenation surgery. J Plast Reconstr Aesthet Surg 75(9):3628–3651

    Article  PubMed  Google Scholar 

  27. Dorante MI, Kollar B, Obed D, Haug V, Fischer S, Pomahac B (2020) Recognizing emotional expression as an outcome measure after face transplant. JAMA Netw Open 3(1):e1919247

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ahmadi N, Niazmand M, Ghasemi A, Mohaghegh S, Motamedian SR (2023) Applications of machine learning in facial cosmetic surgeries: a scoping review. Aesthet Plastic Surg 2023:1–17

    Google Scholar 

  29. Eldaly AS, Avila FR, Torres-Guzman RA et al (2022) Simulation and artificial intelligence in rhinoplasty: a systematic review. Aesthetic Plast Surg 46(5):2368–2377

    Article  PubMed  Google Scholar 

  30. Gibstein AR, Chen K, Nakfoor B et al (2020) Facelift surgery turns back the clock: artificial intelligence and patient satisfaction quantitate value of procedure type and specific techniques. Aesthetic Surg J 41(9):987–999

    Article  Google Scholar 

  31. Adolphs R (2002) Neural systems for recognizing emotion. Curr Opin Neurobiol 12(2):169–177

    Article  CAS  PubMed  Google Scholar 

  32. Boonipat T, Yan M, Bite U (2023) Artificial intelligence for evaluation of emotions behind face masks. Plast Reconstr Surg 151(2):354e–356e

    Article  CAS  PubMed  Google Scholar 

  33. Levitan CA, Rusk I, Jonas-Delson D et al (2022) Mask wearing affects emotion perception. i-Perception 13(3):20416695221107390

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Funding

The authors have no financial disclosures to make. No funding was received for this article.

Author information

Author notes

  1. Agnes Zhu and Thanapoom Boonipat have contributed equally.

Authors and Affiliations

  1. Mayo Clinic Alix School of Medicine, Mayo Clinic Alix School of Medicine, 200 First St. SW, Rochester, MN, 55905, USA

    Agnes Zhu

  2. Department of Plastic Surgery, Mayo Clinic, Rochester, MN, USA

    Thanapoom Boonipat, Sai Cherukuri & Uldis Bite

  3. Division of Plastic and Reconstructive Surgery, Saint Louis University, St. Louis, MO, USA

    Jason Lin

Authors
  1. Agnes Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thanapoom Boonipat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sai Cherukuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jason Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Uldis Bite
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Agnes Zhu.

Ethics declarations

Conflict of interest

No conflicts of interest to disclose. The authors declare that they have no conflicts of interest to disclose.

Human and Animal Rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed Consent

For this type of study informed consent is not required. Patient photos were taken with the patient’s consent.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, A., Boonipat, T., Cherukuri, S. et al. How Brow Rotation Affects Emotional Expression Utilizing Artificial Intelligence. Aesth Plast Surg 47, 2552–2560 (2023). https://doi.org/10.1007/s00266-023-03615-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00266-023-03615-5

Keywords

Search

Navigation