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INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS

Year 2025, Volume: 88 Issue: 4, 270 - 279, 30.10.2025
https://doi.org/10.26650/IUITFD.1663570

Abstract

Objectives: To investigate the temporal verification performance of the facial recognition systems after septorhinoplasty.

Material and Method: The study population included male and female patients who underwent septorhinoplasty at our institution between January 2022 and December 2023. Pre and postoperative photographs were taken at 1, 2, and 4 weeks using the same camera, under the same distance, and under the same lighting conditions. In this techniqueagnostic study, the analysis focused on the overall effect of the procedure rather than the impact of specific surgical manoeuvres. The change over time (preoperative, postoperative weeks 1, 2, 4) was compared based on the mean distance values in the face recognition systems.

Results: The evaluation was conducted on 119 patients, comprising 75 females and 44 males with a mean age of 26.9±7.34 years (range, 1856 years). When the accuracy rates of the face recognition systems were evaluated, the highest performance rate was obtained with the Euclidean metric for the VGGFace system (94.85%). Among the face extraction methods, the RetinaFace (99.40%) and Mtcnn (99.19%) methods had the highest accuracy rates with the Euclid ean metric in the VGGFace face recognition system. There was a significant correlation between the mean distance value (0.378) in the preoperativepostoperative 2nd week evaluation (02) and the mean distance value (0.279) in the 2nd−4th week evaluation (r=0.747, p=0.004).

Conclusions: The alteration of facial components and appearance following septorhinoplasty remains a challenge for postoperative biometric verification using current facial recognition technologies. Rhinologists should be aware of the relationship between septorhinoplasty and facial recognition systems.

References

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  • Çelikhasi C, Ulu A, Sayar A. Comparison of DNA sequences using simulation. Veri Bilimi 2018;1(1):15-9. google scholar
  • Öztürk Y. Content based comparison of traditional methods and convolutional neural networks in medical images. Doctoral thesis. Sakarya: Sakarya University; 2019. google scholar
  • Mistry Y, Ingole DT, Ingole MD. Content based image retrieval using hybrid features and various distance metric. J Electr Syst Inf Technol 2018;5(3):874-88. google scholar
  • Khosla G, Rajpal N, Singh J. Evaluation of Euclidean and Manhanttan metrics in content based image retrieval system. 2nd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE; 2015. New Delhi, India. google scholar
  • Sokolova M, Japkowicz N, Szpakowicz S. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. Australasian joint conference on artificial intelligence.2006. Berlin, Heidelberg. google scholar
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  • Ologunde R. Plastic surgery and the biometric e-passport: Implications for facial recognition. Plast Surg Hand Surg 2015;49(2):127. google scholar
  • Rathgeb C, Dogan D, Stockhardt F, De Marsico M, Busch C. Plastic surgery: An obstacle for deep face recognition? IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020. Seattle, WA, USA. google scholar
  • Oloyede MO, Hancke GP, Myburgh HC. A review on face recognition systems: Recent approaches and challenges. Multimed Tools Appl 2020;79:27891-922. google scholar
  • Erdogmus N, Kose N, Dugelay J-L. Impact analysis of nose alterations on 2d and 3d face recognition. 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP). IEEE; 2012. Banff, AB, Canada. google scholar
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SEPTORİNOPLASTİ VE YÜZ TANIMA SİSTEMLERİ ARASINDAKİ İLİŞKİNİN ARAŞTIRILMASI

Year 2025, Volume: 88 Issue: 4, 270 - 279, 30.10.2025
https://doi.org/10.26650/IUITFD.1663570

Abstract

Amaç: Bu çalışmanın amacı septorinoplasti sonrası yüz tanıma sistemlerinin zamansal doğrulama performansını araştırmaktır.

Gereç ve Yöntemler: Çalışma popülasyonu, Ocak 2022 ile Aralık 2023 arasında kurumumuzda septorinoplasti geçiren erkek ve kadın hastaları içermektedir. Ameliyat öncesi ve sonrası fotoğraflar, aynı kamera kullanılarak, aynı mesafe ve ışık koşullarında 1, 2 ve 4. haftalarda çekilmiştir. Ameliyat öncesi ve sonrası fotoğraflar, sep torinoplastinin genel etkisini teknikagnostik bir yaklaşımla değer lendirmek üzere sekiz farklı yüz tanıma sistemi kullanılarak analiz edilmiştir. Zaman içindeki değişim (ameliyat öncesi, ameliyat son rası 1, 2, 4. haftalar) yüz tanıma sistemlerindeki ortalama mesafe değerlerine göre karşılaştırılmıştır.

Bulgular: Değerlendirme, ortalama yaşları 26,9±7,34 yıl (aralığı, 1856 yıl) olan 75 kadın ve 44 erkek olmak üzere toplam 119 hastadan yapılmıştır. Yüz tanıma sistemlerinin doğruluk oranları değerlendirildiğinde, en yüksek performans oranı VGGFace sis temi için Öklid metriği ile elde edildi (%94,85). Yüz çıkarma yöntemleri arasında, Retinaface (%99,40) ve Mtcnn (%99,19) yön temleri, VGGFace yüz tanıma sisteminde Öklid metriği en yüksek doğruluk oranlarına sahipti. Ameliyat öncesi sonrası 2. hafta değerlendirmesinde (02) ortalama mesafe değeri (0,378) ile 2.4. hafta değerlendirmesinde ortalama mesafe değeri (0,279) arasında anlamlı bir korelasyon vardı (r=0,747, p=0,004).

Sonuç: Septorinoplasti sonrası yüz bileşenlerinin ve görünümünün değişmesi, mevcut yüz tanıma teknolojileriyle ameliyat sonrası biyometrik doğrulama için bir zorluk olmaya devam etmektedir. Rinologlar septorinoplasti ile yüz tanıma sis temleri arasındaki ilişkinin farkında olmalıdır.

References

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  • Singh R, Vatsa M, Bhatt HS, Bharadwaj S, Noore A, Nooreyezdan SS. Plastic surgery: A new dimension to face recognition. IEEE T Inf Foren Sec 2010;5(3):441-8. google scholar
  • Zuo KJ, Saun TJ, Forrest CR. Facial recognition technology: a primer for plastic surgeons. Plast Reconstr Surg 2019;143(6):1298-306. google scholar
  • Bansal A, Shetty NP. Matching before and after surgery faces. Procedia Comput. Sci 2018;132:141-8. google scholar
  • Ricanek K, Tesafaye T. Morph: a longitudinal image database of normal adult age-progression. 7th international conference on automatic face and gesture recognition. 2018. Southampton, UK. google scholar
  • Singh R, Vatsa M, Noore A. Effect of plastic surgery on face recognition: A preliminary study. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. 2009. Miami, FL, USA. google scholar
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  • Chude-Olisah CC, Sulong GB, Chude-Okonkwo UAK, Hashim SZM. Edge- based representation and recognition for surgically altered face images. 7th International Conference on Signal Processing and Communication Systems (ICSPCS). 2013. Carrara, Australia. google scholar
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  • Kim CH, Most SP. Photography and photodocumentation for the rhinoplasty patient. Clin Plast Surg 2022;49(1):13-22. google scholar
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  • Amos B, Ludwiczuk B, Satyanarayanan M. Openface: A general-purpose face recognition library with mobile applications. CMU School of Computer Science 2016;6(2):20. google scholar
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  • Keras insightface. 2023 Aug 30. https://github.com/leondgarse/Keras_ insightface google scholar
  • Kaur P, Krishan K, Sharma SK, Kanchan T. Facial-recognition algorithms: A literature review. Med Sci Law 2020;60(2):131-9. google scholar
  • Sitepu SE, Jati G, Alhamidi MR, Caesarendra W, Jatmiko W. FaceNet with RetinaFace to Identify Masked Face. 6th International Workshop on Big Data and Information Security (IWBIS).2021. Depok, Indonesia. google scholar
  • Şirin Y. Efficient 2d and 3d Image Retrieval with Enhanced Skeleton Points. [Doctoral thesis]. Ankara: TOBB University; 2016. google scholar
  • Çelikhasi C, Ulu A, Sayar A. Comparison of DNA sequences using simulation. Veri Bilimi 2018;1(1):15-9. google scholar
  • Öztürk Y. Content based comparison of traditional methods and convolutional neural networks in medical images. Doctoral thesis. Sakarya: Sakarya University; 2019. google scholar
  • Mistry Y, Ingole DT, Ingole MD. Content based image retrieval using hybrid features and various distance metric. J Electr Syst Inf Technol 2018;5(3):874-88. google scholar
  • Khosla G, Rajpal N, Singh J. Evaluation of Euclidean and Manhanttan metrics in content based image retrieval system. 2nd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE; 2015. New Delhi, India. google scholar
  • Sokolova M, Japkowicz N, Szpakowicz S. Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. Australasian joint conference on artificial intelligence.2006. Berlin, Heidelberg. google scholar
  • Lee J, Jung S, Lee H, Seo J, Choi Y, Bae H, et al. Quantitative anatomical analysis of facial expression using a 3D motion capture system: Application to cosmetic surgery and facial recognition technology. Clin Anat 2015;28(6):735-44. google scholar
  • Ologunde R. Plastic surgery and the biometric e-passport: Implications for facial recognition. Plast Surg Hand Surg 2015;49(2):127. google scholar
  • Rathgeb C, Dogan D, Stockhardt F, De Marsico M, Busch C. Plastic surgery: An obstacle for deep face recognition? IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020. Seattle, WA, USA. google scholar
  • Oloyede MO, Hancke GP, Myburgh HC. A review on face recognition systems: Recent approaches and challenges. Multimed Tools Appl 2020;79:27891-922. google scholar
  • Erdogmus N, Kose N, Dugelay J-L. Impact analysis of nose alterations on 2d and 3d face recognition. 2012 IEEE 14th International Workshop on Multimedia Signal Processing (MMSP). IEEE; 2012. Banff, AB, Canada. google scholar
  • Wolen J. Impact assessment of facial recognition algorithms’ performance when modifying nose dimensions. Thesis. Virginia: West Virginia University. 2015. google scholar
  • Pavri S, Zhu VZ, Steinbacher DM. Postoperative edema resolution following rhinoplasty: a three-dimensional morphometric assessment. Plast Reconstr Surg 2016;138(6):973-9. google scholar
  • Suri S, Sankaran A, Vatsa M, Singh R. On matching faces with alterations due to plastic surgery and disguise.IEEE 9th International Conference on Biometrics Theory, Applications and Systems. IEEE; 2018; Redondo Beach, CA, USA. google scholar
  • Ali ASO, Sagayan V, Malik A, Aziz A. Proposed face recognition system after plastic surgery. IET Comput Vis 2016;10(5):344-50. google scholar
  • Sabharwal T, Gupta R, Son LH, Kumar R, Jha S. Recognition of surgically altered face images: an empirical analysis on recent advances. Artif Intell Rev 2019;52:1009–40. google scholar
  • Deot N, Kiprovski A, Hatala A, Obayemi Jr A, Suryadevara A, Davila RO. Evaluation of mobile and digital single‐lens reflex photography for facial surgical analysis. Laryngoscope 2023;133(10):2590-6. google scholar
  • Derakhshan A, Gadkaree SK, Barbarite ER, Lindeborg MM, Bhama PK, Shaye DA. Quantifying facial distortion in modern digital photography. Laryngoscope 2023;134(3):1234-8. google scholar
There are 50 citations in total.

Details

Primary Language English
Subjects Plastic Reconstructive and Aesthetic Surgery, Otorhinolaryngology
Journal Section RESEARCH
Authors

Nurullah Türe 0000-0002-1664-5634

Buğra Subaşı 0000-0002-7666-612X

Serel Akyol This is me

Emre Güngör 0000-0003-4278-6294

Cemal Aksoy 0000-0001-6273-2053

Publication Date October 30, 2025
Submission Date March 30, 2025
Acceptance Date September 22, 2025
Published in Issue Year 2025 Volume: 88 Issue: 4

Cite

APA Türe, N., Subaşı, B., Akyol, S., … Güngör, E. (2025). INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS. Journal of Istanbul Faculty of Medicine, 88(4), 270-279. https://doi.org/10.26650/IUITFD.1663570
AMA Türe N, Subaşı B, Akyol S, Güngör E, Aksoy C. INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS. İst Tıp Fak Derg. October 2025;88(4):270-279. doi:10.26650/IUITFD.1663570
Chicago Türe, Nurullah, Buğra Subaşı, Serel Akyol, Emre Güngör, and Cemal Aksoy. “INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS”. Journal of Istanbul Faculty of Medicine 88, no. 4 (October 2025): 270-79. https://doi.org/10.26650/IUITFD.1663570.
EndNote Türe N, Subaşı B, Akyol S, Güngör E, Aksoy C (October 1, 2025) INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS. Journal of Istanbul Faculty of Medicine 88 4 270–279.
IEEE N. Türe, B. Subaşı, S. Akyol, E. Güngör, and C. Aksoy, “INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS”, İst Tıp Fak Derg, vol. 88, no. 4, pp. 270–279, 2025, doi: 10.26650/IUITFD.1663570.
ISNAD Türe, Nurullah et al. “INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS”. Journal of Istanbul Faculty of Medicine 88/4 (October2025), 270-279. https://doi.org/10.26650/IUITFD.1663570.
JAMA Türe N, Subaşı B, Akyol S, Güngör E, Aksoy C. INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS. İst Tıp Fak Derg. 2025;88:270–279.
MLA Türe, Nurullah et al. “INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS”. Journal of Istanbul Faculty of Medicine, vol. 88, no. 4, 2025, pp. 270-9, doi:10.26650/IUITFD.1663570.
Vancouver Türe N, Subaşı B, Akyol S, Güngör E, Aksoy C. INVESTIGATION OF THE RELATIONSHIP BETWEEN SEPTORHINOPLASTY AND FACIAL RECOGNITION SYSTEMS. İst Tıp Fak Derg. 2025;88(4):270-9.

Contact information and address

Addressi: İ.Ü. İstanbul Tıp Fakültesi Dekanlığı, Turgut Özal Cad. 34093 Çapa, Fatih, İstanbul, TÜRKİYE

Email: itfdergisi@istanbul.edu.tr

Phone: +90 212 414 21 61