Utvidet returrett til 31. januar 2025

Face Recognition Using Raspberry Pi

Face Recognition Using Raspberry Piav Khaaza M Unnisha Begum
Om Face Recognition Using Raspberry Pi

This Book summarizes the experimental prototype of the embedded image-capturing system with Raspberry Pi system. The graphics capabilities of the Raspberry Pi are roughly equivalent to the level of performance of the Xbox of 2001. The Raspberry Pi chip, operating at 700 MHz by default, will not become hot enough to need a heat sink or special cooling. The SoC is stacked underneath the RAM chip, so only its edge is visible. Level 2 cache is 128 KB, used primarily by the GPU, not the CPU. This system is smaller, lighter and with lower power consumption, so it is more convenient than the PC-based face recognition system. Because of the open-source code, it is freer to do software development on Linux. Experimental results show that it¿s an effective method of using a Raspberry Pi board to actualize an embedded image-capturing system.

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  • Språk:
  • Engelsk
  • ISBN:
  • 9786205522295
  • Bindende:
  • Paperback
  • Sider:
  • 76
  • Utgitt:
  • 31. mars 2023
  • Dimensjoner:
  • 150x5x220 mm.
  • Vekt:
  • 131 g.
  • BLACK NOVEMBER
  Gratis frakt
Leveringstid: 2-4 uker
Forventet levering: 19. desember 2024

Beskrivelse av Face Recognition Using Raspberry Pi

This Book summarizes the experimental prototype of the embedded image-capturing system with Raspberry Pi system. The graphics capabilities of the Raspberry Pi are roughly equivalent to the level of performance of the Xbox of 2001. The Raspberry Pi chip, operating at 700 MHz by default, will not become hot enough to need a heat sink or special cooling. The SoC is stacked underneath the RAM chip, so only its edge is visible. Level 2 cache is 128 KB, used primarily by the GPU, not the CPU. This system is smaller, lighter and with lower power consumption, so it is more convenient than the PC-based face recognition system. Because of the open-source code, it is freer to do software development on Linux. Experimental results show that it¿s an effective method of using a Raspberry Pi board to actualize an embedded image-capturing system.

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