Graduate School and Research Center in Digital Sciences

Facial makeup detection technique based on texture and shape analysis

Kose, Neslihan; Apvrille, Ludovic; Dugelay, Jean-Luc

FG 2015, 11th IEEE International Conference on Automatic Face and Gesture Recognition, May 4-8, 2015, Ljubljana, Slovenia

Recent studies show that the performances of face recognition systems degrade in presence of makeup on face. In this paper, a facial makeup detector is proposed to further reduce the impact of makeup in face recognition. The performance of the proposed technique is tested using three publicly available facial makeup databases. The proposed technique extracts a feature vector that captures the shape and texture characteristics of the input face. After feature extraction, two types of classifiers (i.e. SVM and Alligator) are applied for comparison purposes. In this study, we observed that both classifiers provide significant makeup detection accuracy. There are only few studies regarding facial makeup detection in the state-of-the art. The proposed technique is novel and outperforms the state-of-the art significantly.

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Title:Facial makeup detection technique based on texture and shape analysis
Department:Digital Security
Eurecom ref:4494
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Bibtex: @inproceedings{EURECOM+4494, doi = {}, year = {2015}, title = {{F}acial makeup detection technique based on texture and shape analysis}, author = {{K}ose, {N}eslihan and {A}pvrille, {L}udovic and {D}ugelay, {J}ean-{L}uc}, booktitle = {{FG} 2015, 11th {IEEE} {I}nternational {C}onference on {A}utomatic {F}ace and {G}esture {R}ecognition, {M}ay 4-8, 2015, {L}jubljana, {S}lovenia}, address = {{L}jubljana, {SLOVENIA}}, month = {05}, url = {} }
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