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Face Recognition Efficiency Enhancements Using Tensorflow and WebAssembly: A Practical Approach

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1478)


In this research paper we have studied the use of WebAssembly technology applied to the problem of facial recognition in real world Human-Computer Interaction (HCI) applications. Multiple parameters determining the system were tested using a large image data set. Experiments have shown how, with careful choice of system parameters, very high levels of accuracy can be achieved for large populations, even using only a few pre-stored images per person. In addition, a web application was developed to compare the efficiency of facial recognition using different backends of Tensorflow.js in the browser. The results show that WebAssembly technology is perfectly operational for use in this area and provides user experience improvements in terms of efficiency and stability. In the coming months, WebAssembly is expected to incorporate two relevant developments: Single Instruction Multiple Data (SIMD) and Multithreading (MT). Our experiments show that by enabling these features WebAssembly can provide a very suitable technical solution for in-browser face recognition.


  • Webassembly
  • Wasm
  • Face recognition
  • Tensorflow

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  • DOI: 10.1007/978-3-030-92325-9_7
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Correspondence to Ricardo Martín Manso .

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Martín Manso, R., Escrivá Gallardo, P. (2021). Face Recognition Efficiency Enhancements Using Tensorflow and WebAssembly: A Practical Approach. In: Ruiz, P.H., Agredo-Delgado, V., Kawamoto, A.L.S. (eds) Human-Computer Interaction. HCI-COLLAB 2021. Communications in Computer and Information Science, vol 1478. Springer, Cham.

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