Development and analysis of embedded face recognition system using Raspberry Pi
Human Face is the most visible part which can be used to recognize persons. There are many available systems for face recognition in the market, but they are bulky and expensive. The implementation of face recognition techniques in an embedded system is a very important aspect. This project involve...
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my-unimap-618352019-09-12T06:29:52Z Development and analysis of embedded face recognition system using Raspberry Pi Falah Hassan, Alwan Human Face is the most visible part which can be used to recognize persons. There are many available systems for face recognition in the market, but they are bulky and expensive. The implementation of face recognition techniques in an embedded system is a very important aspect. This project involves design of a real-time, portable, low embedded cost face recognition system. Implementation and analysis of face recognition techniques on an embedded system, the development phase consists of Single Board Computer (SBC, Raspberry Pi (Model A) as process unite, and GNU/Linux based Embedded Raspbian Operating system is used as application development platform. This project focuses to apply the face recognition algorithm that is suitable with Raspberry Pi (Model A) The proposed system is implemented using ARM11 processor and inefficient memory on Raspberry Pi (Model A) board, to get an acceptable performance of the system, the images are captured at resolution (320×240), the system needs ≈ 2.1 sec to process the captured images, The performance of the embedded system is done by evaluating detection time and recognition time (is 1.75 sec, between 0.29 sec to 0.74 sec) respectively, together with CPU utilization and RAM utilization (33%, 17.75%) for detection and (36.5%, 22%) for recognition. Results obtained shows that the overall performance on the embedded system can be increased when motion detection techniques is applied. Universiti Malaysia Perlis (UniMAP) 2015 Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/61835 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61835/1/Page%201-24.pdf 9e1d177d73c3f8a64164a417cf083b49 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61835/2/Full%20text.pdf 6cad2dff0d457147f6d7ab793371798d http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61835/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 Human face recognition Raspberry Pi (Computer) Embedded system Face recognition system Face recognition system -- Design and construction School of Computer and Communication Engineering |
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Universiti Malaysia Perlis |
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UniMAP Institutional Repository |
| language |
English |
| topic |
Human face recognition Raspberry Pi (Computer) Embedded system Face recognition system Face recognition system -- Design and construction |
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Human face recognition Raspberry Pi (Computer) Embedded system Face recognition system Face recognition system -- Design and construction Falah Hassan, Alwan Development and analysis of embedded face recognition system using Raspberry Pi |
| description |
Human Face is the most visible part which can be used to recognize persons. There are many available systems for face recognition in the market, but they are bulky and
expensive. The implementation of face recognition techniques in an embedded system is a very important aspect. This project involves design of a real-time, portable, low embedded cost face recognition system. Implementation and analysis of face
recognition techniques on an embedded system, the development phase consists of
Single Board Computer (SBC, Raspberry Pi (Model A) as process unite, and
GNU/Linux based Embedded Raspbian Operating system is used as application
development platform. This project focuses to apply the face recognition algorithm that
is suitable with Raspberry Pi (Model A) The proposed system is implemented using
ARM11 processor and inefficient memory on Raspberry Pi (Model A) board, to get an
acceptable performance of the system, the images are captured at resolution (320×240),
the system needs ≈ 2.1 sec to process the captured images, The performance of the
embedded system is done by evaluating detection time and recognition time (is 1.75 sec,
between 0.29 sec to 0.74 sec) respectively, together with CPU utilization and RAM
utilization (33%, 17.75%) for detection and (36.5%, 22%) for recognition. Results
obtained shows that the overall performance on the embedded system can be increased
when motion detection techniques is applied. |
| format |
Thesis |
| author |
Falah Hassan, Alwan |
| author_facet |
Falah Hassan, Alwan |
| author_sort |
Falah Hassan, Alwan |
| title |
Development and analysis of embedded face recognition system using Raspberry Pi |
| title_short |
Development and analysis of embedded face recognition system using Raspberry Pi |
| title_full |
Development and analysis of embedded face recognition system using Raspberry Pi |
| title_fullStr |
Development and analysis of embedded face recognition system using Raspberry Pi |
| title_full_unstemmed |
Development and analysis of embedded face recognition system using Raspberry Pi |
| title_sort |
development and analysis of embedded face recognition system using raspberry pi |
| granting_institution |
Universiti Malaysia Perlis (UniMAP) |
| granting_department |
School of Computer and Communication Engineering |
| url |
http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61835/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/61835/2/Full%20text.pdf |
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