Convolutional Neural Networks (CNNs) for Facial Recognition with Black and White Images

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@article{1708845,
      author = {Aniket Tripathi, Mit Patel, Tejas Murthal, Ajay Dodhi, Nupur Doshi},
      title = {Convolutional Neural Networks (CNNs) for Facial Recognition with Black and White Images},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {7},
      number = {6},
      pages = {537-549},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708845.pdf},
      abstract = {Face recognition is becoming the core technology in contemporary computer vision, with a broad variety of applications ranging from biometric verification to intelligent surveillance systems. While excellent results have been obtained using color (RGB) images, grayscale or black and white images continue to maintain importance in most areas, particularly where budget constraints, available infrastructure, or privacy concerns limit the availability of high-quality color information. This study analyzes the performance of Convolutional Neural Networks (CNNs) for facial recognition on black and white images. CNNs have been shown to excel in the domain of visual pattern recognition as a result of their ability to represent features hierarchically. With grayscale images, CNNs exploit edge, texture, and spatial features without the burdensome weighting of color, often yielding more computationally demanding models. The article introduces CNN models that are tailored to perform with grayscale face recognition, e.g., adjustments to guarantee accuracy even in the absence of chromatic information. Dominant issues such as reduction of richness of information, lighting variation, and contrast normalization are explained together with their counter-measures such as data augmentation and preprocessing techniques. The article also delineates some applications in real life where grayscale face recognition is particularly valuable, such as monitoring at low light, analysis of archives with images, and application in lowpower edge devices. The outcomes illustrate the efficiency and stability of CNN-based systems under grayscale conditions and demonstrate that color information, although beneficial, is not always necessary for accurate face recognition. The study concludes with the identification of the potential for investigating light-weight CNN architectures and transfer learning techniques specially for monochromatic datasets. },
      keywords = {Convolutional Neural Networks (CNNs), Facial Recognition, Grayscale Facial Images, Black and White Image Processing, Deep Learning for Face Recognition, Face Detection in Grayscale, CNN based Face Identification, Low-Resolution Face Recognition},
      month = {December},
  }