Facial Makeup Detection Technique Based on Texture and Shape Analysis

Abstract :

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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https://hal.telecom-paristech.fr/hal-02287132
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Submitted on : Friday, September 13, 2019 - 4:37:50 PM
Last modification on : Monday, September 16, 2019 - 11:35:32 AM

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  • HAL Id : hal-02287132, version 1

Citation

Neslihan Kose, Jean-Luc Dugelay, Ludovic Apvrille. Facial Makeup Detection Technique Based on Texture and Shape Analysis. 11th IEEE Conference on Automatic Face and Gesture RecognitionFG'2015, May 2015, Ljubljana, Slovenia. ⟨hal-02287132⟩

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