Impact of Makeup on Automated Face Analysis Systems

My research deals with the impact of facial cosmetics on automated biometric systems:
  • analysis of the impact, 
  • makeup detection,
  • design of algorithms that reduce this impact.
Facial makeup has the ability to alter the appearance of a person.  We established the negative impact of such alteration on the matching accuracy of automated face recognition systems (see Makeup datasets and Baseline Results), as well as automated gender and age estimation algorithms.
Moreover, we designed a method to automatically detect the presence of makeup in face images, based on shape, texture and color characteristics of a face image.
Further, we presented an adaptive pre-processing scheme that exploits knowledge of the presence or absence of facial makeup to improve the matching accuracy of a face matcher.

Makeup for Spoofing

I won the Tabula Rasa Spoofing Attack Award, which was featured in the news.


Makeup to enhance facial aesthetics


Makeup for Age Alteration

age  age2

Related Scientific Papers

  • C. Chen, A. Dantcheva, A. Ross, "Impact of facial cosmetics on automatic gender and age estimation algorithms," Proc. of 9th International Conference on Computer Vision Theory and Applications (VISAPP), (Lisbon, Portugal), January 2014.
  • C. Chen, A. Dantcheva, A. Ross, "Automatic Facial Makeup Detection with Application in Face Recognition," Proc. of 6th IAPR International Conference on Biometrics (ICB), (Madrid, Spain), June 2013.
  • A. Dantcheva, C. Chen, A. Ross, "Can Facial Cosmetics Affect the Matching Accuracy of Face Recognition Systems?," Proc. of 5th IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), (Washington DC, USA), September 2012.
  • A. Dantcheva, C. Chen, A. Ross, "Makeup challenges automated face recognition systems," SPIE Newsroom 2013, Defense and Security. DOI: 10.1117/1.1201303.004795

Soft Biometrics

My Ph.D. work presented the concept of soft biometric systems (SBSs), as systems that employ weak semantic human traits towards human (re-)identification, database search pruning and facial aesthetics prediction. Such traits include gender, age, presence of glasses, hair and skin color. SBSs inherit the non intrusiveness and computational efficiency, which allow for fast, enrolment-free and pose-invariant biometric analysis, even in the absence of consent and cooperation of the surveillance subject.

Assessment of Facial Aesthetics

We studied the interrelation between, on the one hand, subjective perception of female facial aesthetics, and on the other hand, selected objective parameters that include facial features, photo-quality, as well as non-permanent facial characteristics. The approach jointly considered both previous results on photo quality and beauty assessment, as well as non-permanent facial characteristics and expressions. 
Golden Ratio