Face Recognition Using Raspberry Pi

Khaaza M Unnisha Begum

ISBN 10: 6205522292 / ISBN 13: 9786205522295

Published by Scholars' Press, 2023

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - 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.

Seller Inventory # 9786205522295

Bibliographic Details

• Title: Face Recognition Using Raspberry Pi
• Publisher: Scholars' Press
• Publication Date: 2023
• Language: English
• ISBN 10: 6205522292
• ISBN 13: 9786205522295
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 5D millimeters 220 millimeters width by 150 millimeters height by 5 millimeters depth
• Weight: 131 grams

About this title

Synopsis

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.