Towards a Real-Time Facial Analysis System

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Facial analysis is an active research area in computer vision, with many practical applications. Most of the existing studies focus on addressing one specific task and maximizing its performance. For a complete facial analysis system, one needs to solve these tasks efficiently to ensure a smooth experience. In this work, we present a system-level design of a real-time facial analysis system. With a collection of deep neural networks for object detection, classification, and regression, the system recognizes age, gender, facial expression, and facial similarity for each person that appears in the camera view. We investigate the parallelization and interplay of individual tasks. Results on common off-the-shelf architecture show that the system's accuracy is comparable to the state-of-the-art methods, and the recognition speed satisfies real-time requirements. Moreover, we propose a multitask network for jointly predicting the first three attributes, i.e., age, gender, and facial expression. Source code and trained models are available at

Original languageEnglish
Title of host publicationIEEE 23rd International Workshop on Multimedia Signal Processing, MMSP 2021
ISBN (Electronic)9781665432870
Publication statusPublished - 2021
Publication typeA4 Article in a conference publication
EventIEEE International Workshop on Multimedia Signal Processing -
Duration: 1 Jan 1900 → …

Publication series

NameIEEE International Workshop on Multimedia Signal Processing
ISSN (Print)2473-3628


ConferenceIEEE International Workshop on Multimedia Signal Processing
Period1/01/00 → …


  • face detection
  • face recognition
  • facial similarity
  • real-time system

Publication forum classification

  • Publication forum level 1

ASJC Scopus subject areas

  • Signal Processing
  • Safety, Risk, Reliability and Quality
  • Media Technology


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