Compact convolutional neural network cascade for face detection

I. A. Kalinovskii, V. G. Spitsyn

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


This paper presents a new solution to the frontal face detection problem based on a compact convolutional neural networks cascade. Test results on an FDDB dataset show that it is able to compete with state-of-the-art algorithms. This proposed detector is implemented using three technologies: SSE/AVX/AVX2 instruction sets for Intel CPUs, Nvidia CUDA, and OpenCL. The detection speed of our approach exceeds considerably all the existing CPU-based and GPU-based algorithms. Thanks to its high computational efficiency, our detector can process 4K Ultra HD video stream in real time (up to 27 fps) on mobile platforms while searching objects with a dimension of 60×60 pixels or higher. At the same time, its processing speed is almost independent of the background and the number of objects in a scene. This is achieved by asynchronous computation of stages in the cascade.

Original languageEnglish
Pages (from-to)375-387
Number of pages13
JournalCEUR Workshop Proceedings
Publication statusPublished - 2016


  • Cascade classifiers
  • Convolutional neural networks
  • Deep learning
  • Face detection

ASJC Scopus subject areas

  • Computer Science(all)

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