CITATION.cff

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cff-version: 1.2.0
title: hand-face-detector
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: ALVARO LEANDRO
    family-names: CAVALCANTE CARNEIRO
    email: leandro0807@live.com
    affiliation: São Paulo State University
    orcid: 'https://orcid.org/0000-0002-9485-7229'
identifiers:
  - type: url
    value: >-
      https://www.esann.org/sites/default/files/proceedings/2023/ES2023-185.pdf
    description: Paper
repository-code: 'https://github.com/AlvaroCavalcante/hand-face-detector'
abstract: >-
  Object detection is an important preprocessing technique
  for

  sign language recognition, allowing focus on the most
  important parts of

  the image. This paper introduces a new large-scale dataset
  for hand and

  face detection in sign language context, mitigating the
  lack of data for this

  problem. We evaluated different object detection
  architectures to find the

  best trade-off between computational cost and mean Average
  Precision

  (mAP). The proposed dataset contains 477,480 annotated
  images. The

  most accurate detector (CenterNet) achieved an mAP of
  96.7%. Furthermore, the optimizations made to the models
  reduced the inference time

  up to 74% in the best scenario
keywords:
  - deep-learning
  - tensorflow
  - sign-language
  - face-detection
  - object-detection
  - datasets
  - hand-detection
  - sign-language-recognition