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Face Recognition with Disguise and Makeup Variations Using Image Processing and Machine Learning

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Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1440))

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Abstract

Face recognition is a research challenge continuous and has seen colossal development during the last two decades. While coming algorithms keep on achieving improved the performance, a greater part of the face recognition systems are receptive to failure under disguise and makeup variations that is one of the common challenging covariates of facial recognition. In past researches, some algorithms show promising results on the existing disguise datasets, still, most of the disguise datasets include images with limited variations (oftentimes captured in controlled settings). This not simulate a real-world scenario, wherever both the intended/ unintended unconstrained disguises and makeup are encountered by a face recognition systems. In this paper, the disguised and makeup faces database (DMFD) is used. In order to handle this problem, One of simple, yet efficient ways for extracting face image features is (LBPH), Principal Component Analysis (PCA) that was majorly utilized in pattern recognition. Also, the technique of Linear Discriminant Analysis (LDA) employed for overcoming PCA limitations was efficiently used in face recognition. Further, classification is employed following the feature extraction. The Naïve Bayes, KNN and Random forest RF algorithms are used. The results paper show the effectiveness and generalization of the proposed system on the Disguise and makeup face database (DMFD) and the features which are extracted by means of (LDA) with (RF) provided the better results of (F-measure, Recall, and Precision).

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Acknowledgments

My sincere thanks and gratitude to Dr. Ali mohsin Al-juboori for supervision and technical support during the project.

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Authors and Affiliations

  1. College of Computer Science and Information Technology, Al-Qadisiyah University, Al Diwaniyah, Iraq

    Farah Jawad Al-ghanim & Ali mohsin Al-juboori

Authors
  1. Farah Jawad Al-ghanim
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  2. Ali mohsin Al-juboori
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Correspondence to Farah Jawad Al-ghanim or Ali mohsin Al-juboori .

Editor information

Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P. K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

  6. MVPS's Karmaveer Adv. Baburao Ganpatrao Thakare College of Engineering, Nashik, Maharashtra, India

    V. R. Sonawane

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Al-ghanim, F.J., Al-juboori, A. (2021). Face Recognition with Disguise and Makeup Variations Using Image Processing and Machine Learning. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_35

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  • DOI: https://doi.org/10.1007/978-3-030-81462-5_35

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