Human Face Recognition in Digital Images

وص ﻲﺘﻨ ،ﺶـ ودﺪ بﻮـ

يﺮﻴ يﺎـ شو ردﺖ

دﻮـ .

ًﺎﺘﻳﺎﻬ

ﻲـﺳ ﻪﻜﺒ زاﻼ

[26 .

رد ﺖـﻴﻠﻣ صﺎـﺧيﺎـﻫ

يﺮﻴ شورﻪﺑ ﻨـﺳيﺎﻫ

ًﺎﻨﻤ ـﻳريارادﺮﻳوﺎـﺼﺗ

هﺮـﻬﭼي ﺪـﻌﺘﻣيﺎـﻫ ـﺧﺖـﻴﻌﻧﺎﻣوﺖﻴﻌﻣﺎ

شزﻮـﻣآﻪ ﻴﮔدﺎـﻳو

ﺖـﻴﻠﻣرددﺮﺑرﺎـﻛ ـﻫ

دز ﺶـﺨﺑرد . ور3

ﺶـ ﺖـﺳﻮﭘيﺪـﻨﺑ

ﺶ 6 ﻲـﻣنﺎـﻴﺑ ـﺷ

ﺶﺨﺑ ﻪﻳارا8 ﻬﻧو

ﻲـﮔﮋﻳو ﺳﺪﻨﻫيﺎـﻫ

شورﻪـﻳ ﺒـﺷيﺎـﻫ

ﺪﻨ ] 28 - 29 [ ﻼﺜـﻣ .

ﻲﻣهدﺎﻔﺘﺳ ﺪـﻨﻨﻛ 6]

ددﺮﺑرﺎـﻛياﺮـﺑﻲﻳﺰﺟ ﻴﮔدﺎﻳوشزﻮﻣآﻪﺑي ﺖـﺳايرادﺮـﺑ ﻤـﺿ .

يوﺎـﺣصﺎـﺧودراﺪﻧ ﺖ 97 ﻲﻟا% 98 ﺎﺟﺎﺑ%

وﺖـﺳ ﻪـﺑيزﺎـﻴﻧ ـﻛياﺮـﺑﻲـﻳﺰﺟي

ﺶﺨﺑرد ﻲﻣ2

ادﺮﭘ

ﺶﺨ ـﺨﺑهﻮـﺤﻧو4 ﺶـﺨﺑردﻲﺘـﺳﻮﭘﻲ

ﺶﻳﺎﻣ رﺪﻴﺑﺮﺠﺗيﺎﻫ

ووﺖﺳﻮﭘﮓﻧرس ] 26 [ اراﻦﻴﻨﭽﻤﻫ.

ﻲـﮕﻧريﺎـﻀ ﻨﺘـﺴﻫ

ﺳايﺮﺳاﺮﺳﺮﻳﻮﺼﺗ

لﺎﺘﻴﺠﻳد

ي ناﺮ

ﺟيﺮﻴﮔدﺎﻳردنآﺰﻛ يزﺎﻴﻧوﺖﺳاﺮﮕﻳديﺎ وﺮﻳﻮﺼ ﺲﻜﻋﻂﻳاﺮﺷ ﻧﺎﺘـﺳاﻲﮕﻧرﺮﻳﻮﺼﺗي ﺖﻗدزاﻢﺘﺴﻴﺳﻪﻛﺪ

ﺳاهﺮﻬﭼﻲﺳﺪﻨﻫت ﺰﻛﺮﻤ نآ ﻳرد يﺮﻴﮔدﺎ

ﺳانﻮﮕﻤ ﺖ .

ﻪﺑ دﻂﺒﺗﺮﻣيﺎﻫرﺎﻛ ﺨﺑردﺖﺳﻮﭘلﺪﻣ

ﺸﺗهﻮﺤ ﻲﺣاﻮﻧﺺﻴﺨ ﺎﻣزآﺞﻳﺎﺘﻧوﺖﺳاه

ﻲﻣيﺮﻴ دﻮﺷ .

ﻂ

ﺨ سﺎﺳاﺮﺑهﺮﻬﭼﺺﻴ

شور ﺎﻫ 95 ﺖﺳا %

ﻀﻓرديرﺎـﻣآيﺎـﻫ ﻠﺤﻣﺖ ﺗﻞﻜﺷوﻲ

د ﺮﻳوﺎﺼﺗ ر

ﻦﻴ ﻪﻟا ﻪﻣ يدﺎﺑآ

ﺪﻫﺎﺷ

، ﺮﻳا ،ناﺮﻬﺗ

ﻲ ﻛﺮﻤﺗﺮﺘﺸﻴﺑﻪﻛدﻮﺷ

شورﺎﺑتوﺎﻔﺘﻣ،هﺮ ﺎﻫ

،ﺮﻳﻮﺼﺗهزاﺪ حﻮﺿو ﺗ ﺼ

ﻪﻧﻮﻤﻧيورﺮﺑﻲﺑﺮ يﺎﻫ

ﻲﻣنﺎﺸﻧ،ﺮﻳﻮﺼﺗ ﺪﻫد

تاﺮﻴﻴﻐﺗﻪﺑﺖﻴﺳﺎﺴ ﺮﻤﺗﺮﺘﺸﻴﺑودراﺪﻧه

ﻲﺘﺳﻮﭘﺮﻳوﺎﺼﺗو ﻤﻫ

ﺑاﺪﺘﺑاﻪﻟﺎﻘﻣ،ﻪﻣادا ﻲﻣحﺮﻃيد دﻮﺷ

.

ددﺮﮕﻴﻣﻪﻳارا5 ﺤﻧ .

هﺮﻬﭼلﺪﻣﻦﻴﺒﻣ7 ﺶﺨﺑر ﻪﺠﻴﺘﻧ9 ﻴﮔ

ﻂﺒﺗﺮﻣ يﺎﻫرﺎﻛ

ﺨﺸﺗﻦﺌﻤﻄﻣﻢﺘﺴﻴ ﺖﺳاﺰ رﻦﻳاﺖﻗد.

لﺪـﻣسﺎﺳاﺮﺑﺎﻳ ﻫ

ﺖﻓﺎﺑنﺎﻴﺑياﺮﺑماﺮﮔ

رد نﺎﺴﻧا

ﻴﻣا

ﻲﺳﺪ

، هﺎﮕﺸﻧاد ﺷ

ﻲﮕﻧرﺮﻳوﺎﺼﺗ ﻲﻣﻪﻳارا

ﺮﻬﭼيﻮﮕﻟاوﻲﮕﻧرﺮ ﺪﻧا،ترﻮﺻرﻮﺤﻣلﻮﺣ ﺶﻳﺎﻣزآﺞﻳﺎ ﺮﺠﺗيﺎﻫ

ﻚﻳردهﺮﻬﭼﻦﻳﺪﻨﭼ

هﺮـﻬﭼ يﺎﻫر ﻂـﺳﻮ ردﻲـ يزﺎـﺳ ﻻﻮـﻤﻌ

،ﺖـﻓﺎ دﻮﺟﻮ ﻲـﮕﻧر ﻲ ﺪﻧﺮﺑ زاﻞﻘ

ارا ـﻳ ﻪ

،ﻻﺎـﺑ ﺴﺣمﺪﻋ

هﺪﻴﭽﻴﭘ صﺎﺧ و

رد دﺎﻬﻨﺸﻴﭘ

ﺶﺨﺑ 5

ﺶﺨﺑ 7

دﻪﻟﺎﻘﻣ

2 ﻛ -

ﻴﺳﻚﻳ ﺰﻛﺮﻤﺘﻣ ﻲﺒﺼﻋ ﮔﻮﺘﺴﻴﻫ

هﺮﻬﭼ يز

ﻲﻋراز ﺎﺿﺮﻴ هﺪﻜ و ﻲﻨﻓ ﺪﻨﻬﻣ

ﺗردﻲﻧﺎﺴﻧاهﺮﻬﭼﺮﻳﻮ لﺪ ﺮﻳوﺎﺼﺗردﺖﺳﻮﭘ ﺣﺶﺧﺮﭼﻪﻳواز،هﺮﻬ

ﻲﻣرﺎﻜﺷآﺰﻴ دزﺎﺳ

ﺎﺘﻧ.

ﺟوﻲﻧﺎﻣﺰﻤﻫﺎﺑ ﭼدﻮ

ﺖﺳﻮﭘﮓﻧر، .

ﭼﺺﻴﺨـﺸﺗرﺎـﻛدﻮ لﺪـﻣ ﺎﺘﺧﺎـﺳوﺎـﻫ ﻮﺗواﺺﻴﺨـﺸﺗﺖﻋ ـﺳﺎﺳامﺎـﮔناﻮـﻨﻋ

اﻮـﺘﺤﻣ هدﺮـﺸﻓ، ﺳ

ﺖـﺳاﻪـﺘﻓﺮﮔ ﻌﻣ .

ﺎﺑ،ﻮـﮕﻟاﻖـﻴﺒﻄﺗ،ﺎﻫ شورﺖﻴﻤﻫ يﺎﻫ

ﻮﻣ

ريزوﺮـﻣاﺮﻳوﺎـﺼﺗ ﻲﻣهﺮﻬﺑهﺮﻬﭼﺺﻴﺨ

ﻲﮕﻧرﺮﻳوﺎﺼ ﻘﺘـﺴﻣ،

ﺮﻳﻮـﺼﺗرددﻮـﺟﻮ شزادﺮـﭘﺖﻋﺮﺳي

زﺎﺳرﺎﻜﺷآ

ﻴﻠﻋ ﻜﺸﻧاد

ﻮﺼﺗﺺﻴﺨﺸﺗياﺮﺑﻻ ﻪﺑ ﺪﻣزاهدﺎﻔﺘﺳاﻞﻴﻟد

ﺲ ﻬﭼﺖﻴﻌﻗﻮﻣ،ﻪﻨﻴﻣز

هﺮﻬﭼد ﻴﻧدﻮﺟﻮﻣيﺎﻫ

ﻂﻳا ﻲﻄﻴﺤﻣﻒﻠﺘﺨﻣ

لﺪﻣ،نﺎﺴﻧاه ﺖﺳﻮﭘ

ﻚﻳد ﻮﺧﻢﺘـﺴﻴﺳ

ﻪ ﻣزاهدﺎﻔﺘﺳاﻞﻴﻟد

ﻪـﺑ ﻋﺮـﺳوترﺪـﻗ

ﺺﻴﺨﺸ هﺮﻬﭼ ﻪـﺑ ﻋ

سﺎـﺳاﺮﺑﺮﻳﻮـﺼﺗﻲ مﺎﺠﻧاﻪﻨﻴﻣزﻦﻳارد

ﻲ ﻲﮔﮋﻳو،لﺪﻣﺮﺑ ﻫ

ﻲﻣ دﻮﺷ ] 3 - 17 [ ﻫا .

ﺗﺮﺘﺸﻴﺑﻪـﻛﻲﻟﺎـﺣر ﺨﺸﺗياﺮﺑﻲﮕﻧرﺮﻳﻮ هﺮﻬﭼيزﺎﺳر ﺼﺗرد

داﺪﻌ هﺮـﻬﭼ ﻮﻣيﺎـﻫ

يارادهﺪﻴﭽﻴﭘتﺎﺒﺳ

تﺎﻋﻼﻃاي ناﺮﻳاﺮﺗﻮﻴﭙﻣﺎﻛ

1395

آ

وﻊﻳﺮﺳك ﻻﺎﺑﺖﻗدﺎﺑ

ﺖ يدﺎﻬﻨﺸﻴﭘشور.

،يزادﺮﭘرﻮﻧﺖ ﺲﭘﮓﻧر

داﺪﻌﺗﺖﻳدوﺪﺤﻣنوﺪ اﺮﺷﺖﺤﺗ،هﺪﻴﭽﻴﭘﻪﻨ ﺖﺳارا .

ﻒﺸﻛ،ﺮﻳﻮﺼﺗش هﺮﻬﭼ

دﺎﺠﻳاﻦﻴﺷﺎﻣﻲﻳﺎﻨﻴ ﺪﺷﺎﺑنﺎﺴﻧاز ﻪﺑﺎﻣا .

نﺪﻴـﺳر،نﺎﺴﻧاﺰﻐ ﻲﻣ ﺪﺳر 1] - [2 ﺸﺗ.

ﻲﺑﺎﻳزﺎﺑ،ﻲﻧاﻮﺧﺐﻟ يرﺎﻴﺴﺑتﺎﻌﻟﺎﻄﻣ شورزا ﻲﻨﺘﺒﻣيﺎﻫ

ﺖﺳﻮﭘﮓﻧ ﻣمﺎﺠﻧا

ردﺖـﺳايﺮﺘﺴﻛﺎﺧ ش ﻮﺼﺗزاﺪﻳﺪﺟيﺎﻫ رﺎﻜﺷآﺖﻬﺟﻲﺷور

ﺲﻜﻋ ﺪﻌﺗويرادﺮﺑ

ﺳﺎﺤﻣنوﺪﺑيدﺎﻬﻨ

ﻋﻠ مﻮ يروﺎﻨﻓوﺶﻧﺎﻳار

ﻲﻤﻠﻋﻪﻳﺮﺸﻧ ﻦﻤﺠﻧا

ﻛ

ﺪﻠﺠﻣ هرﺎﻤﺷ،14

،1

تﺎﺤﻔﺻ 16 - 22

يدﺎﻋﻪﻟﺎﻘﻣ

هﺪﻴ

ﻲﺷورﻪﻟﺎﻘﻣﻦ كﻻﺎﭼ

ﺖﺳانﻮﮕﻤﻫﻲﺘﺳﻮﭘﺮ در ﺖﻴﻌﺿوزاﻞﻘﺘﺴﻣ .

وهﺪﻨﺧﺖﻟﺎﺣﺎﺑ،ﻚ ﺪﺑ

ﺲﭘﺎﺑﻲﻧﺎﺴﻧات ﻪﻨﻴﻣز

ادرﻮﺧﺮﺑﻒﻠﺘﺨﻣﻂﻳاﺮ يﺪﻴﻠﻛت شزادﺮﭘ:

ﻪﻣﺪﻘﻣ

ﻴﺑتﺎﻘﻴﻘﺤﺗﻲﻳﺎﻬﻧ زاﺮﺗآرﺎﻛﺪﻧاﻮﺘﺑﻪﻛ ﻐﻣﻂﺳﻮﺗهﺪﻴﭽﻴﭘر

ﺪﻴﻌﺑﻼﻌﻓﻦ ﻪﺑ

ﺮﻈﻧ

ﺺﻴ

،نﺎﺴﻧاﺖﻳﻮﻫ ﺖﺳانآﺪﻨﻧﺎﻣوﺮ .

هدﺎﻔﺘﺳاﺎﺑهﺮﻬﭼﻒ ﻪ

،ﻲﺒﺼﻋ ﻧروﻖﻤﻋ

ﺧﺮﻳوﺎﺼﺗزاهدﺎﻔﺘﺳ ﺪﻨ 11] - [21 شور .

- 25 [ ﻪﻟﺎﻘﻣﻦﻳارد.

ﺲﭘ ﻂﻳاﺮﺷ،ﻪﻨﻴﻣز

دﻮ شورﻦﻳا. ﻨﺸﻴﭘ

ﻋ ﻧ ﻣ ﺻ ﻣ

ﻴﻜﭼ

ﻦﻳارد ﺮﻳوﺎﺼﺗ ار راﺪﻧ ﻚﻨﻴﻋ توﺎﻔﺘﻣ ﺮﺷرد تﺎﻤﻠﻛ

1 ﻣ -

فﺪﻫ ﺖﺳا رﺎﻴﺴﺑ ﻦﻴﺷﺎﻣ ﻴﺨﺸﺗ ﺮﻳﻮﺼﺗ ﻒﺸﻛ ﻪﻜﺒﺷ ﺳارد ﻨﺘﺴﻫ ] 22 -

ﮓﻧر ﻲﻣ ﻮﺷ

ع اوﻲﻋراز ﻪﻣ آﺑ يدﺎ ردنﺎﺴﻧاهﺮﻬﭼيزﺎﺳرﺎﻜﺷآ لﺎﺘﻴﺠﻳدﺮﻳوﺎﺼﺗ

يدﺎﻋﻪﻟﺎﻘﻣ

ونﺎﺒﻴﺘﺸﭘرادﺮﺑﻦﻴﺷﺎﻣ زا سﻼـﻛ

يﺎـﻀﻓردشزﻮـﻣآرﺎﺘﺧﺎـﺳﺎـﺑﺖـﺳﻮﺑادآيﺪـﻨﺑ

ﺖﺳاهﺪﺷهدﺎﻔﺘﺳاﺰﻴﻧﻒﻠﺘﺨﻣ 28]

.[ لﺪـﻣوماﺮﮔﻮﺘـﺴﻴﻫﻲﻳآرﺎـﻛ ﺺﻴﺨـﺸﺗيﺎـﻫ

لﺪﻣﻦﺘﻓﺎﻳوﺖﺳﻮﭘ ماﺮﮔﻮﺘﺴﻴﻫيﺎﻫ

ﻲﻣﻞﻤﻋﻲﻟﺎﻋرﺎﻴﺴﺑ وﺪﻨﻨﻛ

ﺖﻗدﺶﻳاﺰﻓاﺚﻋﺎﺑ

ﻲﻣﻪﻨﻳﺰﻫﺶﻫﺎﻛو ﺪﻧﻮﺷ

] 30 [ لﺪﻣ . رﺎﻴـﺴﺑﺖـﺳﻮﭘﮓـﻧرسﺎـﺳاﺮﺑﺺﻴﺨـﺸﺗيﺎﻫ

ﻊﻳﺮﺳرﺎﻴﺴﺑودﺎﻤﺘﻋاﻞﺑﺎﻗ،هدﺎﺳ شورزاﺮﺗ

ﺪﻨﺘـﺴﻫﺮﮕﻳديﺎﻫ .

سﺎـﺳاﺮﺑﻲـﺷورﺎـﻣ

هﺮﻬﭼيﻮﮕﻟاوﺖﺳﻮﭘلﺎﻤﺘﺣايﺎﻀﻓ،ﺖﺳﻮﭘﮓﻧر ﻲﻣﻪﻳارا

ﻲـﮔﮋﻳوزاﻪﻛﻢﻴﻫد يﺎـﻫ

ﺎﻗ،ﻲﮔدﺎﺳ ﺖﻋﺮﺳ،دﺎﻤﺘﻋاﺖﻴﻠﺑ ﺲﭘردﺺﻴﺨﺸﺗ،

دﻮـﺟو،هﺪـﻴﭽﻴﭘﻪـﻨﻴﻣز ﺎـﺑهﺮـﻬﭼ

هزاﺪﻧا توﺎﻔﺘﻣيﺎﻫ ﻂﻴﺤﻣﺖﺤﺗو

ﺖﺳارادرﻮﺧﺮﺑﻒﻠﺘﺨﻣيﺎﻫ .

ﻞﻜﺷ 1 هﺮﻬﭼيزﺎﺳرﺎﻜﺷآﻢﺘﻳرﻮﮕﻟا - ﺮﻳوﺎﺼﺗردنﺎﺴﻧا

ﻲﮕﻧر

3 يدﺎﻬﻨﺸﻴﭘ شور -

ردﺎﻣيدﺎﻬﻨﺸﻴﭘشور هدﺎﻴﭘﺖﻳﻮﻫﻦﻴﻴﻌﺗﻢﺘﺴﻴﺳﻚﻳﺐﻟﺎﻗ

ﺖـﺳاهﺪﺷيزﺎﺳ لاور .

ﻞﻜﺷﻢﺘﻳرﻮﮕﻟاﻖﺑﺎﻄﻣرﺎﻛمﺎﺠﻧا ﺖﻓﺎﻳردﺎﺑاﺪﺘﺑاردﻪﻛﺖﺳا1

ﻪـﻧﻮﻤﻧ يﺎـﻫ ﻲﺘـﺳﻮﭘ

جاﺮﺨﺘﺳاو ﺖﺳﻮﭘلﺪﻣ لﺎﻤﺘﺣا-

زﺎﻏآ ﻲﻣ دﻮﺷ ﺰﻛﺮﻣتﺎﺒﺳﺎﺤﻣ;

،مﺮـﺟ دﺎـﻌﺑا وﻪـﻴﺣﺎﻧ

ﻲﻣمﺎﺠﻧافاﺮﺤﻧاﻪﻳواز دﺮﻴﮔ

ﻲﺣاﻮﻧ; ﻲﻣفﺬﺣﺪﺋاز ددﺮﮔ

ﺎﺑ; هﺮﻬﭼيﻮﮕﻟا ﻪﻴﻬﺗ)

هﺪﺷ

ﻪﻧﻮﻤﻧزا ﺎﻫ ﻖﻴﺒﻄﺗ ( ﻲﻣهداد دﻮﺷ

ًﺎﺘﻳﺎﻬﻧ; هﺮﻬﭼ ﻲﻣجاﺮﺨﺘﺳاﻲﻧﺎﺴﻧايﺎﻫ ددﺮـﮔ

شور.

ﻲﻣﺎﻣ ﻪﺑﺪﻧاﻮﺗ ﻳناﻮﻨﻋ شورياﺮـﺑنزوﻢﻛوكﻻﺎﭼﻪﻠﺣﺮﻣﻚ هدﺎﻔﺘـﺳاﺮـﮕﻳديﺎـﻫ

رددﺮﺑرﺎﻛياﺮﺑﻲﻳﺰﺟيﺮﻴﮔدﺎﻳﻪﻠﺣﺮﻣردﺎﻣﺰﻛﺮﻤﺗﺮﺘﺸﻴﺑﻪﻛدﻮﺷ ﺖﻴﻠﻣ

صﺎﺧيﺎﻫ

ﺖﺳانﻮﮕﻤﻫﻲﺘﺳﻮﭘﺮﻳوﺎﺼﺗو .

4 ﺖﺳﻮﭘ لﺪﻣ -

ﻪﺑ ﺶﺨﺑرﻮﻈﻨﻣ ﻲﺣاﻮﻧيﺪﻨﺑ

،ﮓﻧريﺎﻨﺒﻣﺮﺑﻲﺘﺳﻮﭘ ﮓـﻧرلﺪـﻣﻚـﻳﻪـﺑ

ﺎـﺑﺖـﺳﻮﭘ

ﺎﺑداﺮﻓاياﺮﺑﻖﻴﺒﻄﺗﺖﻴﻠﺑﺎﻗ ﮓﻧر

زﺎـﻴﻧتوﺎـﻔﺘﻣيرﻮـﻧﻂﻳاﺮﺷوﻲﺘﺳﻮﭘﻒﻠﺘﺨﻣيﺎﻫ

ﻢﻳراد رد . ﻚـﻳﺶـﺨﺑﻦﻳا ﮓـﻧرلﺪـﻣ

ﻲـﮕﻧريﺎـﻀﻓردﺖـﺳﻮﭘ ياﺮـﺑﻚـﻴﺗﺎﻣوﺮﻛ

ﺶﺨﺑ ﻲﻣﻪﻳاراﻲﺘﺳﻮﭘﻲﺣاﻮﻧيﺪﻨﺑ دﻮﺷ

ﺶﻳﺎﻤﻧ . ﺲـﻜﻋلواﺪﺘﻣRGB

يﺎـﻫ ﻲـﮕﻧر

ﺳﻮﭘﮓﻧرﻒﻴﺻﻮﺗﺖﻬﺟ ﺖﺴﻴﻧﺐﺳﺎﻨﻣﺖ

اﺮﻳز رد ﺎـﻀﻓﻦﻳا هﺪﻧزﺎـﺳﻪـﻔﻟﻮﻣﻪـﺳ نآ

(G, B, R) ﮓﻧرهﺪﻨﻳﺎﻤﻧﺎﻬﻨﺗﻪﻧ

ﺰـﻴﻧﻲﻳﺎﻨـﺷورمﺎﻧﻪﺑيﺮﮕﻳدﺮﺼﻨﻋﺺﺧﺎﺷﻪﻜﻠﺑﺎﻫ

ﻲﻣ ﺪﻨﺷﺎﺑ ﻲﻣﻲﻳﺎﻨﺷور. ﺲﻜﻋردﺪﻧاﻮﺗ

توﺎﻔﺘﻣيﺎﻫ زا

هﺮﻬﭼ ﻚـﻳ دﺮـﻓ ﺰـﻴﻧ توﺎـﻔﺘﻣ

ﻪﻠﺌﺴﻣﻦﻳاﻪﻛﺪﺷﺎﺑ ﻦﻜﻤﻣ

ﺖﺳا رﻮﻧتوﺎﻔﺗزا ﻲﺷﺎﻧﻂﻴﺤﻣ

دﻮﺷ اﺬﻟ. ﻪﻔﻟﻮﻣﻲﻳﺎﻨﺷور

رديدﺎﻤﺘﻋاﻞﺑﺎﻗ ﺎﺳاﺪﺟﻪﻨﻴﻣز

ز ﻲﺣاﻮﻧي وﻲﺘﺳﻮﭘ ﻲﺘﺳﻮﭘﺮﻴﻏ ﺖﺴﻴﻧ ﻦﻳاﻞﺣياﺮﺑ.

ﻲﻣﻞﻜﺸﻣ هدﺎﻔﺘﺳاﻚﻴﺗﺎﻣوﺮﻛيﺎﻀﻓردﺲﻜﻋﺶﻳﺎﻤﻧزاناﻮﺗ ﻧآﻞﻴﻟﺪـﺑدﺮﻛ

ردﻪـﻜ

ﻲﻣﺎﻀﻓﻦﻳا ارﻲﻳﺎﻨﺷورناﻮﺗ

زا ﻪﻔﻟﻮﻣ دﻮﻤﻧﺰﻳﺎﻤﺘﻣﻲﮕﻧريﺎﻫ .

ﮓﻧر ﻪﺑﻚﻴﺗﺎﻣوﺮﻛيﺎﻫ هدﺮﺘﺴﮔﻞﻜﺷ

ﺶﺨﺑياﺮﺑيا رﺎـﻜﺑﺮﻳوﺎـﺼﺗيﺪـﻨﺑ

هدﺮـﺑ

ﻲﻣ ﺪﻧﻮﺷ ﺑﺰﻴﻧيدﺎﻬﻨﺸﻴﭘشورردﻪﻛ ﻪ

ﺶﺨﺑﻪﺑﻲﺑﻮﻠﻄﻣﻮﺤﻧ ﻲﺣاﻮﻧﻪﺑﺮﻳوﺎﺼﺗيﺪﻨﺑ

ﻲﻣﻚﻤﻛﻲﺘﺳﻮﭘﺮﻴﻏوﻲﺘﺳﻮﭘ ﺪﻨﻛ

نﺎﺸﻧتﺎﻘﻴﻘﺤﺗ . ﮓﻧرﻊﻳزﻮﺗﻪﻛهداد

داﺮﻓاﺖﺳﻮﭘ ﻪﻘﺒﻃﻞﺑﺎﻗﻚﻴﺗﺎﻣوﺮﻛﻲﮕﻧريﺎﻀﻓزاﻲﻜﭼﻮﻛهدوﺪﺤﻣردﻒﻠﺘﺨﻣ

ﺖﺳايﺪﻨﺑ ]

11 [ .

ﻪﺑاﺮﻫﺎﻇﻪﭼﺮﮔ ﻲﻣﺮﻈﻧ

ﮓﻧرﻪﻛﺪﺳر ﺎﻣاﺪﺷﺎﺑﺮﻴﻐﺘﻣﻲﻌﻴﺳوهدوﺪﺤﻣردداﺮﻓاﺖﺳﻮﭘ

ﻲﻳﺎﻨﺷورردتوﺎﻔﺗﺎﺗﺪﻧرادتوﺎﻔﺗﺮﺘﻤﻛﻲﻠﻴﺧﮓﻧرظﺎﺤﻟزا ﻪﺑ .

ﮓـﻧرﺮـﮕﻳدترﺎﺒﻋ

رد،سﻮـﺴﺤﻣتوﺎـﻔﺗوﺪﻨﺘـﺴﻫﻚـﻳدﺰﻧﻢﻬﺑرﺎﻴﺴﺑﻒﻠﺘﺨﻣداﺮﻓاﺖﺳﻮﭘ تﺪـﺷ

نآ

ﮓﻧرلﺪﻣزاهدﺎﻔﺘﺳاﻪﺑتﺎﻘﻴﻘﺤﺗﻦﻳاﺞﻳﺎﺘﻧﻪﻛﺖﺳا يﺎﻀﻓردﺖﺳﻮﭘ

ردﻚﻴﺗﺎﻣوﺮﻛ

ﺮﺠﻨﻣيدﺎﻬﻨﺸﻴﭘشور هﺪﺷ

ﺖﺳا .

ﻞﻜﺷ 2 ﮓﻧرﻊﻳزﻮﺗ - ﻒﻠﺘﺨﻣداﺮﻓاﺖﺳﻮﭘ

ﻞﻜﺷ 3 ﺑ - زاﺮ ش رﻧ ﮓ زﻮﺗردﺖﺳﻮﭘ ﻲﺳﻮﮔﻊﻳ

زا ﻪﻧﻮﻤﻧ150 ﻪﺘﻓﺮﮔﻲﺘﺳﻮﭘ زاهﺪﺷ

ﺮﻳﻮﺼﺗ35 ﮓـﻧرﻊـﻳزﻮﺗﻦﻴﻴﻌﺗياﺮﺑﻲﮕﻧر

ﻪـﻧﻮﻤﻧوهﺪـﺷهدﺎﻔﺘـﺳاﻚﻴﺗﺎﻣوﺮﻛيﺎﻀﻓردنﺎﺴﻧاﺖﺳﻮﭘ زاﺎـﻫ

يﺎـﻫداﮋﻧﺎـﺑداﺮـﻓا

،ﻲﻳﺎﻴﺳآنﻮﮔﺎﻧﻮﮔ ﺖـﺳاهﺪـﻳدﺮﮔﺬـﺧاﻦﻴـﺗﻻوﻲﻳﺎﻘﻳﺮﻓآ،يزﺎﻘﻔﻗ،ﻲﻳﺎﭘورا

. ﺲﭙـﺳ

ﻪﻧﻮﻤﻧ يﺎﻫ ﻦﻴﻳﺎﭘﺮﺘﻠﻴﻓزاهدﺎﻔﺘﺳاﺎﺑﻲﺘﺳﻮﭘ ﺰﻳﻮﻧﺶﻫﺎﻛياﺮﺑرﺬﮔ

ﻪﺘﻓﺎﻳدﻮﺒﻬﺑ ﺖـﺳا

.

ﻞﻜﺷ

،2 ﻊﻳزﻮﺗ ﻪﻧﻮﻤﻧﻦﻳاﮓﻧر ﻲـﻣنﺎـﺸﻧﻚﻴﺗﺎﻣوﺮﻛيﺎﻀﻓردارﻲﺘﺳﻮﭘيﺎﻫ

ﺪـﻫد .

ﻞﻜﺷﻲﮕﻧرماﺮﮔﻮﺘﺴﻴﻫ نﺎﺸﻧ2

ﻊﻳزﻮﺗهﺪﻨﻫد ﮓﻧر

يﺎـﻀﻓردﻒـﻠﺘﺨﻣداﺮـﻓاﺖﺳﻮﭘ

ﻲﺼﺨﺸﻣهدوﺪﺤﻣردﻪﻛﺖﺳاﻚﻴﺗﺎﻣوﺮﻛ دﺮﮔ

ﺖـﺳاهﺪﻣآ . ﮓـﻧرﻊـﻳزﻮﺗ ﺎـﺑﺖـﺳﻮﭘ

لﺪﻣ ﻲﺳﻮﮔ N(m,c) و ﻪﺑ ﻪﻄﺑارحﺮﺷ )

1 ﺶﻳﺎﻤﻧ( هداد ﻲـﻣ دﻮـﺷ ﻞﻜـﺷ.

،3 ﻊـﻳزﻮﺗ

شزاﺮﺑﻲﺳﻮﮔ هدادﻖﺑﺎﻄﻣهﺪﺷ

ﺎﻫ ﻲﻣﻪﻳاراار ﺪـﻫد .

ﮓـﻧرلﺪـﻣﻦـﻳايﺮﻴﮔرﺎـﻜﺑﺎـﺑ

وﺖﺳﻮﭘ ﻪﻄﺑارزاهدﺎﻔﺘﺳاﺎﺑ )

2

،( ﻪـﻴﺣﺎﻧﻪـﺑﻞـﺴﻜﻴﭘﺮـﻫﻖﻠﻌﺗلﺎﻤﺘﺣا ردﻲﺘـﺳﻮﭘ

ﻲﻣﺖﺳﺪﺑﺮﻳﻮﺼﺗ ﺪﻳآ

ﮓﻧرلﺪﻣ . ﻲﻣﺖﺳﻮﭘ ﻪـﺑارﻲـﮕﻧرﺮﻳﻮـﺼﺗﺪـﻧاﻮﺗ ﺮﻳﻮـﺼﺗﻚـﻳ

لﺪﺑيﺮﺘﺴﻛﺎﺧ ﺖـﺳﻮﭘلﺎﻤﺘﺣاﺮﮕﻧﺎﻳﺎﻤﻧﻞﺴﻜﻴﭘﺮﻫشزرانآردﻪﻛﺪﻨﻛ

نآندﻮـﺑ

ﺖﺳاﻪﻄﻘﻧ ﻪﻧﺎﺘﺳآﺎﺑ .

يﺮﻴﮔ ﻚﻳ،ﺮﻳﻮﺼﺗﻦﻳازاﻲﻘﻴﺒﻄﺗ ﺮﻳﻮﺼﺗ

ﻲﻣﺖﺳﺪﺑيﺮﻨﻳﺎﺑ ﺪـﻳآ

هﺪﺷﺰﻳﺎﻤﺘﻣنآردﻲﺘﺳﻮﭘﺮﻴﻏوﻲﺘﺳﻮﭘﻪﻴﺣﺎﻧﻪﻛ ﺪﻧا

. يﺮﻴﮔدﺎـﻳﻪـﻠﺣﺮﻣﻦـﻳافﺪـﻫ

ردنآﺖﻗدﺶﻳاﺰﻓاياﺮﺑ ﺖﻴﻠﻣدﺮﺑرﺎﻛ

ﺖﺳانﻮﮕﻤﻫﻲﺘﺳﻮﭘﺮﻳوﺎﺼﺗوصﺎﺧيﺎﻫ .

) 1 (

T

T

mean: m E(x) x (r,b) T:Transpose covariance:C E{(x m)(x m) }

 

  

) 2 (

T 1

T

P(r, b) exp 0.5(x m ) C (x m ) where : x (r, b)

  

    



5 ﺶﺨﺑ - ﻲﺘﺳﻮﭘ يﺪﻨﺑ

ﺖـﺳﻮﭘﺮﻳﻮﺼﺗ -

ﺢﻄـﺳراﺪـﻘﻣنآردﻪـﻛﺖـﺳايﺮﺘـﺴﻛﺎﺧﺮﻳﻮـﺼﺗﻚـﻳلﺎـﻤﺘﺣا

ﺮﻫيﺮﺘﺴﻛﺎﺧ ﻲـﻣﻲﺘـﺳﻮﭘﻪﻴﺣﺎﻧﻪﺑنآﻖﻠﻌﺗلﺎﻤﺘﺣاﺮﮕﻧﺎﻳﺎﻤﻧ،ﻞﺴﻜﻴﭘ

ﺪـﺷﺎﺑ . ﻚـﻳ

ﺮﻳﻮﺼﺗﻪﻧﻮﻤﻧ وﻲﮕﻧر

ﺖﺳﻮﭘﺮﻳﻮﺼﺗ ﺮﻇﺎﻨﺘﻣلﺎﻤﺘﺣا-

ﻞﻜﺷردنآ هدادنﺎﺸﻧ4

هﺪﺷ

وﺖﺳا ﻪﻴﺣﺎﻧﺮﻳﻮﺼﺗنآرد ﻦﺷورﻲﺘﺳﻮﭘ

هﺪـﻳدﺮـﺗ ﻲـﻣ دﻮـﺷ . ﻲﺣاﻮـﻧﻪـﻛﺎـﺠﻧآزا

ياﺪﻳﺪﻧﺎﻛ ﻦﺷورﺮﮕﻳدﻲﺣاﻮﻧﻪﺑﺖﺒﺴﻧﺖﺳﻮﭘ ﻲﻣارﻲﺘﺳﻮﭘﻲﺣاﻮﻧاﺬﻟﺖﺳاﺮﺗ

ناﻮـﺗ

ﻪﻧﺎﺘﺳآﺎﺑ ﺖﺳﻮﭘﺮﻳﻮﺼﺗزايﺮﻴﮔ اﺪﺟلﺎﻤﺘﺣا-

دﺮﻛ .

شزادﺮﭘياﺮﺑ داﺮﻓاﺮﻳوﺎﺼﺗ

ﮓﻧرﺎﺑﻒﻠﺘﺨﻣ يﺎـﻫداﮋﻧزاوتوﺎـﻔﺘﻣﻲﺘـﺳﻮﭘيﺎـﻫ

،ﺰﻳﺎﻤﺘﻣ ﺳﺎﻨﻣ،ﺖﺑﺎﺛﻪﻧﺎﺘﺳآﻚﻳزاهدﺎﻔﺘﺳا ﺖﺴﻴﻧروﺪﻘﻣوﺐ

ﻪﺑﻲﺑﺎﻴﺘﺳدياﺮﺑ . ﻚـﻳ

ﻪﻧﺎﺘﺳآراﺪﻘﻣ ﺮﻫردﻪﻨﻴﻬﺑ

ﻪﻧﺎﺘﺳآﻪﺑزﺎﻴﻧﺮﻳﻮﺼﺗ ﺖﺳاﻲﻘﻴﺒﻄﺗيﺮﻴﮔ

ﻪﻧﺎﺘﺳآرد . يﺮﻴﮔ

ﻪﻠﺣﺮﻣﺶﻫﺎﻛﻲﻘﻴﺒﻄﺗ

ًﺎﻤﻴﻘﺘﺴﻣ،ﻪﻧﺎﺘﺳآراﺪﻘﻣيا ﺚﻋﺎﺑ

ﻒـﺸﻛﻲﺣاﻮـﻧداﺪـﻌﺗﺪـﺷر

ﻲﻣهﺪﺷ ددﺮﮔ ﺮﻫ . ﻮﻧداﺪﻌﺗردﺶﻳاﺰﻓاﻦﻳاﺪﻨﭼ ﻪﺑﻲﺣا

ورﺞﻳرﺪﺗ ﻲﻣﺶﻫﺎﻛﻪﺑ دراﺬﮔ

ﻪﺑهﺪﺷﻒﺸﻛﻲﺣاﻮﻧﺪﺻرداﺮﻳز

% ﻲﻣﻞﻴﻣ100 ﺪﻨﻛ ﻦﻳاﻲﻟو . ﻒﺸﻛﻲﺣاﻮﻧ ردهﺪﺷ

ﻚﭼﻮﻛترﻮﺻ ﻪﻧﺎﺘﺳآراﺪﻘﻣندﻮﺑ

ﻪﺑ ﺪﻨﻫاﻮﺧﺪﺷرﺖﻋﺮﺳ ﻪﺑدﺮﻛ

ﻪﻧﻮﮔ ﻲـﺘﺣﻪـﻛيا

ردﺰﻴﻧارﻲﺘﺳﻮﭘﺮﻴﻏﻲﺣاﻮﻧ ﺮﺑ

ﺖﻓﺮﮔﺪﻨﻫاﻮﺧ ﻪﻧﺎﺘـﺳآراﺪﻘﻣ .

ﻦﻳﺮـﺘﻤﻛنآردﻪـﻛيا

ﺪﺷرناﺰﻴﻣ ﻒﺸﻛﻲﺣاﻮﻧ

ﻪﺑﻢﻴﺷﺎﺑﺪﻫﺎﺷارهﺪﺷ ﻪﻧﺎﺘـﺳآراﺪـﻘﻣناﻮﻨﻋ

ﻲـﻘﻠﺗﻪـﻨﻴﻬﺑ

ﻲﻣ ددﺮﮔ . ﻞﻜﺷرد ﺶﺨﺑﺮﻳﻮﺼﺗ5

هﺪﻫﺎﺸﻣهﺪﺷيﺪﻨﺑ ﻲﻣ

ددﺮﮔ ﻲـﻣﻪﻈﺣﻼﻣ . ﻪـﻤﻫدﻮـﺷ

ﻒﺸﻛﻲﺣاﻮﻧ ﺪﻨﺘﺴﻴﻧﻲﻧﺎﺴﻧاهﺮﻬﭼيوﺎﺣهﺪﺷ

. ﻒـﺸﻛﻲﺣاﻮـﻧزاﻲﺧﺮﺑﻲﺘﺣ هﺪـﺷ

ﺎﺒﺘﺷاﻪﺑﻪﻛﺪﻨﺘﺴﻫﻲﺘﺳﻮﭘﺮﻴﻏﻲﺣاﻮﻧ راﺮﻗﻲﺘﺳﻮﭘﻲﺣاﻮﻧﻦﻴﺑرده

ﻪﺘﻓﺮﮔ ﺪﻧا ﺮﻣاﻦﻳا .

ﻪﺑ ﺖﻫﺎﺒﺷﻞﻴﻟد ﻪﺑﻲﺣاﻮﻧﻦﻳاﮓﻧر

ﺖـﺳاﻲﺘـﺳﻮﭘﻲﺣاﻮـﻧ ﺬـﻟ.

،يﺪـﻌﺑﻞـﺣاﺮﻣردا

ﻢﺘﺴﻴﺳﺮﮔﻮﺠﺘﺴﺟ ﻲﮔﮋﻳوﻲﺧﺮﺑزا

مﺎـﻤﺗردهﺮـﻬﭼﻞـﺤﻣﻦﻴـﻴﻌﺗياﺮـﺑهﺮﻬﭼيﺎﻫ

ﻢﻫﻖﻃﺎﻨﻣ ﻲﻣهدﺎﻔﺘﺳاﺖﺳﻮﭘﮓﻧر ﺪﻨﻛ

.

6 ﻲﺘﺳﻮﭘ ﻲﺣاﻮﻧ -

ﻪﻴﺣﺎﻧﻚﻳ ﻪﺘﺴﺑﻪﻴﺣﺎﻧ،ﺮﻳﻮﺼﺗﻲﺘﺳﻮﭘ يارادﻪﻛﺖﺳايا

ﺎﻳ0 ﺪﺷﺎﺑهﺮﻔﺣ1 ﻦﻳازﺮﻣ .

ﻞﺴﻜﻴﭘﺎﺑﻪﻴﺣﺎﻧ شزراﺎﺑﻲﻳﺎﻫ

ﻲﻣﺺﺨﺸﻣ1 دﻮﺷ

ﻲﻣﻪﻜﻧآﻦﻤﺿ . ﻪـﻴﺣﺎﻧﻚـﻳناﻮـﺗ

ﻪﺑارﻲﺘﺳﻮﭘ ﺶﺨﺑترﻮﺻ

ﻪﺑيﺎﻫ ردﻞﺼﺘﻣﻢﻫ ﺖﻓﺮﮔﺮﻈﻧ

هﺮﻔﺣ . ﺮﻳﻮـﺼﺗﻚـﻳردﺎـﻫ

ﻞﺴﻜﻴﭘ،يﺮﻨﻳﺎﺑ ﺪﻨﺘـﺴﻫﺮﻔـﺻراﺪـﻘﻣﺎﺑﻲﻳﺎﻫ

. شورزاﻲﺣاﻮـﻧداﺪـﻌﺗﻦﻴـﻴﻌﺗياﺮـﺑ

ﺐﺴﭼﺮﺑ يراﺬﮔ ) ﻲﮕﻳﺎﺴﻤﻫ8 هدﺎﻔﺘﺳا (

هﺪﺷ ﺖﺳا ﺮﻫياﺮﺑ . ﻪـﻛﻲﺗرﻮـﺻردﻞﺴﻜﻴﭘ

نآﻲﮕﻳﺎﺴﻤﻫﻚﻳﻞﻗاﺪﺣ ﻪـﺑارﻞـﺴﻜﻴﭘنآﺪﺷﺎﺑﺐﺴﭼﺮﺑياراد

ﻲﮕﻳﺎـﺴﻤﻫنﺎـﻤﻫ

ﺖﺒﺴﻧ ﺮﻴﻏردوهﺪﺷهداد ﻦﻳا

ﺻ ﻲـﻣهدﺎﻔﺘـﺳاﺪـﻳﺪﺟﺐـﺴﭼﺮﺑﻚﻳزاترﻮ دﻮـﺷ

رد .

ﺐﺴﭼﺮﺑنﺎﻳﺎﭘ ﻦﻴﻴﻌﺗﻲﺣاﻮﻧداﺪﻌﺗوهﺪﺷهدﺮﻤﺷﺎﻫ

ﻲﻣ ددﺮﮔ . ﺮـﻫيزﺎـﺳاﺪﺟياﺮـﺑ

ﭼﺮﺑزا،ﺶﺨﺑ ﻪـﻴﻘﺑﺶـﺨﺑنآﻦﺘﻓﺎﻳزاﺪﻌﺑوهدﺎﻔﺘﺳاﻪﻘﻄﻨﻣنآﺐﺴ

ﻲﺣاﻮـﻧ ﺮﻔـﺻ

ﻲﻣ دﻮﺷ ﻲﻣﻦﻴﻴﻌﺗﺲﭙﺳ . ﻒـﺸﻛﻪﻴﺣﺎﻧﺎﻳآﻪﻛددﺮﮔ

هﺪـﺷ ﻚـﻳهﺪـﻨﻳﺎﻤﻧﺮﻈﻧدرﻮـﻣ

ﺮﻴﺧﺎﻳﺖﺴﻫهﺮﻬﭼ ﻞﻜﺷ)

6 .(

6 - 1 هﺮﻔﺣ -

هﺮﻔﺣداﺪﻌﺗﻪﺒﺳﺎﺤﻣياﺮﺑ ﻚﻳيﺎﻫ

ﺎﻧنآﺮﻟوادﺪﻋزا،ﻪﻴﺣﺎﻧ ﻪـﻄﺑارﻖﺑﺎـﻄﻣﻪـﻴﺣ

) 3 (

هﺪﺷهدﺎﻔﺘﺳا ﺖﺳا

،ﻪﻄﺑارنآرد .

،ﺮﻟوادﺪﻋ E نﺎـﺸﻧC

ﺶـﺨﺑهﺪـﻨﻫد ﻪـﺑيﺎـﻫ

ﻢـﻫ

وﻞﺼﺘﻣ هﺮﻔﺣداﺪﻌﺗهﺪﻨﻳﺎﻤﻧ H

ﺖﺳاﻞﺼﺘﻣﻲﺣاﻮﻧيﺎﻫ .

داﺪـﻌﺗيدﺎﻬﻨﺸﻴﭘشوررد

ﻲﻣﻲﻫدراﺪﻘﻣﻚﻳدﺪﻋﺎﺑﻞﺼﺘﻣﻲﺣاﻮﻧ اﺮﻳزدﻮﺷ

رﺎﻛوﺮﺳﻪﻴﺣﺎﻧﻚﻳﺎﺑﻪﻠﺣﺮﻣﺮﻫرد

ﻪﻄﺑارﺲﭘﻢﻳراد )

4 ﺖﺷادﻢﻴﻫاﻮﺧار ( هﺮﻔﺣداﺪﻌﺗﻢﺘﺴﻴﺳﺮﮔا .

ﻚـﻳزاﺮﺘـﺸﻴﺑارﺎﻫ

ﺺﻴﺨﺸﺗ ﻲﻣمﺎﺠﻧاﻮﮕﻟاﻖﻴﺒﻄﺗﻪﻠﺣﺮﻣ،ﺪﻫد ﻦﻳاﺮﻴﻏردودﺮﻴﮔ

ﺮﻈﻧدرﻮﻣﻪﻴﺣﺎﻧترﻮﺻ

ﺪﻨﻳآﺮﻓﻪﻣادازا ﻲﻣفﺬﺣ

ددﺮﮔ .

ﻒﻟا) ﻲﮕﻧرﺮﻳﻮﺼﺗ( ﻲﻠﺻا ب) ﺖﺳﻮﭘﺮﻳﻮﺼﺗ ( لﺎﻤﺘﺣا-

ﻞﻜﺷ 4 ﺖﺳﻮﭘﺮﻳﻮﺼﺗوﻲﮕﻧرﺮﻳﻮﺼﺗﻚﻳﻪﻧﻮﻤﻧ - لﺎﻤﺘﺣا -

ﻒﻟا) ﺮﻳﻮﺼﺗ ( ﺶﺨﺑ يﺪﻨﺑ هﺪﺷ ب) ﺖﺳﻮﭘﺮﻳﻮﺼﺗ ( لﺎﻤﺘﺣا-

ﻞﻜﺷ 5 ﺶﺨﺑﺮﻳﻮﺼﺗ - ﺖﺳﻮﭘﺮﻳﻮﺼﺗوﻲﻧﺎﺴﻧاهﺪﺷيﺪﻨﺑ

لﺎﻤﺘﺣا-

ﻒﻟا) ﻪﻴﺣﺎﻧ ( ﻲﺘﺳﻮﭘ ب) ﻲﺣاﻮﻧ ( ﺶﺨﺑ يﺪﻨﺑ هﺪﺷ

ﻞﻜﺷ 6 ﺎﻧ - ﺣ ﻪﻴ ﺨﺑﻲﺣاﻮﻧوهﺪﺷﺺﺨﺸﻣﻲﺘﺳﻮﭘ ﺶ

هﺪﺷيﺪﻨﺑ

) 3 ( E C H

) 4 (

H 1 E 

6 - 2 مﺮﺟ ﺰﻛﺮﻣ -

ﺮﻫﻪﻌﻟﺎﻄﻣياﺮﺑ دﻪﻴﺣﺎﻧ

ر

،نآﺐﺳﺎﻨﻣشورﻪﻛﺪﻳآﺖﺳﺪﺑﻪﻴﺣﺎﻧنآﺰﻛﺮﻣﺪﻳﺎﺑاﺪﺘﺑا

ﺖﺳاﻪﻴﺣﺎﻧمﺮﺟﺰﻛﺮﻣﻪﺒﺳﺎﺤﻣ .

ﺰـﻛﺮﻣيﺮﻨﻳﺎـﺑﺮﻳوﺎﺼﺗرداﺮﻳز ﺰـﻛﺮﻣنﺎـﻤﻫ،مﺮـﺟ

ع اوﻲﻋراز ﻪﻣ آﺑ يدﺎ ردنﺎﺴﻧاهﺮﻬﭼيزﺎﺳرﺎﻜﺷآ لﺎﺘﻴﺠﻳدﺮﻳوﺎﺼﺗ

يدﺎﻋﻪﻟﺎﻘﻣ

ﺖﺳاﻪﻴﺣﺎﻧ مﺮﺟﺰﻛﺮﻣ .



^{x , y}



ﻪﻄﺑارحﺮﺷﻪﺑﻲﺣاﻮﻧﻦﻳا )

5 ﻲﻣﻪﺒﺳﺎﺤﻣ ( دﻮﺷ

ﻪـﻛ

رد نآ ﺲﻳﺮﺗﺎﻣﻚﻳB m n

نﺎﺸﻧ ﻪﻴﺣﺎﻧهﺪﻨﻫد و

ﻞـﺴﻜﻴﭘداﺪﻌﺗA شﻮﻣﺎـﺧيﺎـﻫ

ﺖﺳاﻪﻴﺣﺎﻧنآ .

) 5 (

n m

 

n m

i 1 i 1 i 1 i 1

1 1

x jB i, j y iB[i, j]

A   A  



 



 

6 - 3 ﻖﻴﺒﻄﺗ - ﺖﻬﺟ

ﺎﺠﻧآزا هﺮﻬﭼﻲﺧﺮﺑﻪﻛ ﻲﻣفاﺮﺤﻧاﻪﻳوازيارادﺮﻳﻮﺼﺗيﺎﻫ

ﻪـﺑﻲﺑﺎﻴﺘـﺳدياﺮﺑ،ﺪﻨﺷﺎﺑ

ﺪﻳﺎﺑﺮﺘﻬﺑﻪﺠﻴﺘﻧ ﺮﻳﻮﺼﺗ

هﺮﻬﭼﺖﻬﺟﻖﺑﺎﻄﻣﻮﮕﻟا ﺪﺧﺮﭽﺑ

ﻪـﻳوازﻪﺒـﺳﺎﺤﻣﻪـﺑزﺎﻴﻧاﺬﻟ

ﺖﺳاهﺮﻬﭼفاﺮﺤﻧا ﻦﻳاياﺮﺑ .

ﻪﺒﺗﺮﻣيﺰﻛﺮﻣروﺎﺘﺸﮔزارﺎﻛ هدﺎﻔﺘﺳامود

ﻪﻳوازوهﺪﺷ

ﻂﺧ ﺎـﺑﻪـﻴﺣﺎﻧطﺎﻘﻧزاﻪﻠﺻﺎﻓتﺎﻌﺑﺮﻣعﻮﻤﺠﻣﻦﻳﺮﺘﻤﻛيارادﻪﻛارﻪﻴﺣﺎﻧزاهﺪﻧرﺬﮔ

هزاﺪﻧا،ﺪﺷﺎﺑﺎﻫرﻮﺤﻣ ﻲﻣيﺮﻴﮔ

ﺪﻨﻛ زﺐﻴﺗﺮﺗﻦﻳاﻪﺑ . فاﺮﺤﻧاﻪﻳوا

θ ﻞﻜﺷ) 7 ﺑﻪ ( حﺮـﺷ

ﻪﻄﺑار ) 6 ﻲﻣﻪﺒﺳﺎﺤﻣ ( دﻮﺷ

.

6 - 4 ﻪﻴﺣﺎﻧ دﺎﻌﺑا -

مﺪﻋﻞﻤﺤﺗﺖﻬﺟﻮﺠﺘﺴﺟدرﻮﻣﺮﻳﻮﺼﺗدﺎﻌﺑاﺎﺑﻖﺑﺎﻄﺗوﻮﮕﻟاﺮﻳﻮﺼﺗهزاﺪﻧاﺮﻴﻴﻐﺗياﺮﺑ ﺮﻳﻮﺼﺗردندﺮﮔﺮﻳزﻲﺣاﻮﻧوندﺮﮔﻪﻴﺣﺎﻧﺶﺷﻮﭘ ﻪـﺑزﺎﻴﻧ

ضﺮـﻋولﻮـﻃﻪﺒـﺳﺎﺤﻣ

ﺖﺳاﻪﻴﺣﺎﻧ هﺮﻔﺣاﺪﺘﺑارﺎﻛﻦﻳاياﺮﺑ .

ﻧيﺎﻫ درﻮﻣﻪﻴﺣﺎ ﺮﭘﺮﻈﻧ هدﺎﻔﺘـﺳاﺎﺑﺲﭙﺳ،هﺪﺷ

رﺎـﻬﭼﺖـﻛﺮﺣزاهدﺎﻔﺘﺳاﺎﺑوهدروآرديدﻮﻤﻋﻸﻣﺎﻛﺖﻟﺎﺣﻪﺑارنآفاﺮﺤﻧاﻪﻳواززا هرﺎﺷا ﺖﻬﺟرﺎﻬﭼردﺮﮔ ﺖﺳاروﭗﭼ،ﻦﻴﻳﺎﭘ،ﻻﺎﺑ)

( ﻪـﺑدرﻮـﺧﺮﺑﺎـﺑﻪـﻴﺣﺎﻧيﺎﻫزﺮﻣ،

ﻞﺴﻜﻴﭘ ﺖﻓﺎﻳ،ﻦﺷوريﺎﻫ

ﻲـﻣﻪﺒـﺳﺎﺤﻣضﺮـﻋولﻮـﻃوهﺪـﺷ دﻮـﺷ

. دﻮـﺒﻬﺑياﺮـﺑ

ـــﻴﻠﻤﻋ ﻢﻴﻤـــﺼﺗتﺎ هدﺎﻔﺘـــﺳاﻪـــﻴﺣﺎﻧضﺮـــﻋﻪـــﺑعﺎـــﻔﺗراﻢﻴـــﺴﻘﺗزايﺮـــﻴﮔ

ﻲﻣ دﻮـﺷ

Ratio(Height / Width)

. ـﺒﺴﻧﻦـﻳا ـ نﺎـﺴﻧاياﺮـﺑﺖ رديدﺪـﻋ،ﺎـﻫ

دوﺪﺣ ﺖﺳا1 .

 

n m 2

ij i 1 j 1

c y ' B[i, j]

 



 

b

1 / 2 arctana c_

 

) 6 (

 

n m 2

i j i 1 j 1

a x ' B [ i , j]

 



 

n m

 

i j i j i 1 j 1

b 2 x ' y ' B i , j

 



 

y ' y y و

x ' x x

ﻢﻛياﺮﺑ تﺎﻔﻠﺗندﺮﻛ ﻪﺑ

ﻦـﻳاياﺮـﺑﻦﻴﻳﺎـﭘﺪـﺣﻚﻳوﻻﺎﺑﺪﺣﻚﻳﻲﺑﺮﺠﺗرﻮﻃ

بﺎﺨﺘﻧاﺖﺒﺴﻧ هﺪﺷ

ﺖﺳا نﺎﺸﻧﻲﺑﺮﺠﺗﺞﻳﺎﺘﻧ . ﻦﻴﻳﺎـﭘﺪـﺣﻪﻨﻴﻬﺑراﺪﻘﻣﻪﻛداد

و0.6

راﺪﻘﻣ دوﺪﺣردﻻﺎﺑﺪﺣﻪﻨﻴﻬﺑ ﺖﺳا1.2

. ﺮـﻈﻧدرﻮـﻣﻪـﻴﺣﺎﻧﻪﻛدراددﻮﺟويدراﻮﻣ

هﺮﻬﭼﻪﻴﺣﺎﻧﻚﻳهﺪﻨﻳﺎﻤﻧ زاﺖﺒﺴﻧﻦﻳاﺎﻣاﺪﺷﺎﺑيا

ﺖـﺳاﺮﺗﻻﺎﺑ1.2 .

ردﻪﻠﺌـﺴﻣﻦـﻳا

ﺶﺨﺑﻪﻛﺖﺳاﻲﻌﻗاﻮﻣ هﺪﻴﺷﻮﭘنآﺮﻳزوندﺮﮔزاﻲﻳﺎﻫ

ﺪﺷﺎﺒﻧ ﻪﺑدراﻮﻣﻦﻳارد . ﻪﺑﺮﺠﺗ

ﻲﻣ ﻮﺗ ارﺖﺒﺴﻧنا جرﺎﺧﻲﺣاﻮﻧوضﺮﻓ1.2

زا دﻮﻤﻧفﺬﺣﺮﻳﻮﺼﺗزاارﺖﺒﺴﻧﻦﻳا .

7 لﺪﻣ - هﺮﻬﭼ

ﻢﻬﻣ ﺖـﻬﺟاﺪـﻳﺪﻧﺎﻛﻪـﻴﺣﺎﻧﺎـﺑهﺮـﻬﭼيﻮـﮕﻟاﻖﻴﺒﻄﺗﻪﻠﺣﺮﻣشورﻦﻳاﻪﻠﺣﺮﻣﻦﻳﺮﺗ

ﻢﻴﻤﺼﺗ ﺖﺳاﻲﻳﺎﻬﻧيﺮﻴﮔ ﻞﻜﺷردﻪﻛﻲﻳﻮﮕﻟاﺮﻳﻮﺼﺗ .

ﻲﻣﻪﻈﺣﻼﻣ8 ﻦﻴﮕﻧﺎﻴﻣزادﻮﺷ

26 يﻮـﮕﻟاناﻮـﻨﻌﺑوهﺪـﺷدﺎـﺠﻳاﺶـﻳرنوﺪـﺑدﺮـﻣونزخرمﺎﻤﺗﻒﻠﺘﺨﻣﺮﻳﻮﺼﺗ

ﺖﺧﺎﻨﺷ هﺮﻬﺑنآزاهﺮﻬﭼ ﻲﻣيرادﺮﺑ

ددﺮﮔ ﻪﻧﻮﻤﻧﺮﮔاﻪﺘﺒﻟا . ﻲـﻣﺪـﺑﺎﻳﺶﻳاﺰﻓاﺎﻫ

ناﻮـﺗ

ﺮﻫياﺮﺑ ﻲـﻣﺮﺘﺸﻴﺑﺺﻴﺨﺸﺗﺖﻗدوﺖﻓﺎﻳارنآهﺮﻬﭼصﺎﺧيﻮﮕﻟاﻲﺘﻠﻣ دﻮـﺷ

رد .

ﻪـﻴﺣﺎﻧﺎﺑﺮﻇﺎﻨﺘﻣﻲﺣاﻮﻧﺮﻳوﺎﺼﺗﻖﻴﺒﻄﺗﻲﮕﻧﻮﮕﭼﺶﺨﺑﻦﻳا ﻮـﮕﻟاﺮﻳﻮـﺼﺗوﻲﺘـﺳﻮﭘ

ﻲﻣمﺎﺠﻧا دﺮﻴﮔ . ﺶـﺨﺑياﺮـﺑ

ـﺳﻮﭘﻪـﻴﺣﺎﻧﺎـﺑﺮﻇﺎـﻨﺘﻣيﺎـﻫ هﺮـﻔﺣاﺪـﺘﺑاردﻲﺘ

يﺎـﻫ

رددﻮﺟﻮﻣ ﺮﭘﺮﻳﻮﺼﺗ

ﻲـﻣبﺮـﺿﻲﻠـﺻاﺮﻳﻮـﺼﺗردﻞـﺻﺎﺣﺮﻳﻮﺼﺗﺲﭙﺳهﺪﺷ دﻮـﺷ

ﻞﻜﺷ) 9 .(

يﻮﮕﻟا ﺪﻳﺎﺑهﺮﻬﭼ هدادراﺮﻗﺮﻈﻧدرﻮﻣﻪﻴﺣﺎﻧيورﺮﺑﻼﻣﺎﻛ دﻮﺷ

. رﻮـﻈﻨﻣﻦـﻳاياﺮﺑ

نآﺪﻳﺎﺑ ﻪﺒﺳﺎﺤﻣضﺮﻋولﻮﻃسﺎﺳاﺮﺑار ﺖﻤﺴﻗردهﺪﺷ

ﺮﻴﻴﻐﺗ،ﻲﻠﺒﻗيﺎﻫ هزاﺪﻧا

داد .

يﻮﮕﻟا هﺮﻬﭼ اﺪﻴﭘﺶﺧﺮﭼﻪﻴﺣﺎﻧفاﺮﺤﻧاﻪﻳوازهزاﺪﻧاﻪﺑﺪﻳﺎﺑ نآﻲﻓﺎﺿاﻲﺣاﻮﻧوﺪﻨﻛ

دﻮﺷفﺬﺣ ﻞﻜﺷ)

10 ﻲﻣﻪﺒﺳﺎﺤﻣﻪﺘﻓﺎﻳﺮﻴﻴﻐﺗ،يﻮﮕﻟاهﺮﻬﭼﺰﻛﺮﻣﺲﭙﺳ .(

ردودﻮﺷ

هﺎﻴﺳﺮﻳﻮﺼﺗﺖﻳﺎﻬﻧ ﺮﺑﻮﮕﻟاﻪﺘﻓﺎﻳﺮﻴﻴﻐﺗﺮﻳﻮﺼﺗﻪﻛيﺪﻴﻔﺳو

هدادراﺮـﻗنآيور هﺪـﺷ

ﻪﺑ ﻲﻣﺖﺳد ﺪﻳآ . يﺪـﻌﺑودﻲﮕﺘـﺴﺒﻤﻫﺐﻳﺮـﺿ ﺲﻳﺮﺗﺎـﻣودr

ﺮﻳﻮـﺼﺗ Am n

و Bm n

ﻪﻄﺑارﻖﺑﺎﻄﻣ )

7 ﻲﻣﻪﺒﺳﺎﺤﻣ ( ددﺮﮔ

ﺞﻳﺎـﺘﻧزاهدﺎﻔﺘﺳاﺎﺑ . ﺮﻳوﺎـﺼﺗزاﻞـﺻﺎﺣﻲـﺑﺮﺠﺗ

ﻢﻴﻤﺼﺗﺖﻬﺟﺐﻳﺮﺿﻦﻳاياﺮﺑﻦﻴﻳﺎﭘﻪﻧﺎﺘﺳآراﺪﻘﻣ،ﻒﻠﺘﺨﻣ هﺮﻬﭼﺖﻬﺟﻲﻳﺎﻬﻧيﺮﻴﮔ

ﻪﻴﺣﺎﻧندﻮﺑ ﺪﺷﻦﻴﻴﻌﺗ0.58

رد . ﻢﺘﺴﻴﺳ،ﺖﺒﺜﻣﺦﺳﺎﭘترﻮﺻ ﻪـﻴﺣﺎﻧردﺎـﻛﻚـﻳﺎـﺑ

ﻲﻣﺺﺨﺸﻣارﺮﻈﻧدرﻮﻣ ﺪﻨﻛ

هﺮﻬﭼﺎﺑﻲﺟوﺮﺧﺮﻳﻮﺼﺗﻪﻧﻮﻤﻧ . ﻞﻜﺷردرﺎﻴﺴﺑيﺎﻫ

11

ﻲﻣهﺪﻫﺎﺸﻣ دﻮﺷ

.

ﻞﻜﺷ 7 ﺮﻳﻮﺼﺗردهﺮﻬﭼﺶﺧﺮﭼﻪﻳواز -

ﻞﻜﺷ 8 نﺎﺴﻧاهﺮﻬﭼﻲﻳﺎﻬﻧيﻮﮕﻟا -

ﻒﻟا) ﻲﻠﺻاﺮﻳﻮﺼﺗ ( ب) ﻪﻴﺣﺎﻧ ( ﻲﺘﺳﻮﭘ ج) رﺪﺑﺮﺿ ( ﺮﻳﻮﺼﺗ ﻲﻠﺻا

ﻞﻜﺷ 9 ﻲﺘﺳﻮﭘﻪﻴﺣﺎﻧﺺﻴﺨﺸﺗﻞﺣاﺮﻣ -

) 7 (

  

   

m n m n

m m

2 2

m n m n

m n m n

A A B B

r

A A B B

 

        

 

   

ﻞﻜﺷ 10 - ﻒﻟا) يﻮﮕﻟا( ﺶﺧﺮﭼ ﻪﺘﻓﺎﻳ ب) ﺪﺋاوزفﺬﺣ (

ﻞﻜﺷ 11 ﺎﺑﺺﻴﺨﺸﺗﻪﺠﻴﺘﻧ- يارادﺮﻳﻮﺼﺗ

هﺮﻬﭼ22

لوﺪﺟ -1 ﻪﻧﻮﻤﻧ ﺮﻳوﺎﺼﺗيورﺮﺑتﺎﺸﻳﺎﻣزآيﺎﻫ ﺎﻫﺮﺘﻣارﺎﭘﺬﺧاياﺮﺑ

ﺶﻳﺎﻣزآ

لﻮﻃﺖﺒﺴﻧهزاﺪﻧا ﻪﻴﺣﺎﻧضﺮﻋﻪﺑ

ﺐﻳﺮﺿهزاﺪﻧا ﻲﮕﺘﺴﺒﻤﻫ هﺮﻬﭼﺪﺻرد

هﺪﺷﻒﺸﻛ ﺮﻳوﺎﺼﺗداﺪﻌﺗ هﺮﻬﭼﺮﻴﻏﻲﻔﺸﻛ

1 0.2 0.58

80 4

2 0.4 0.58

80

3

3 0.6 0.58

80

2

4 0.8 0.58

75

2

5 1.0 0.58

80

1

لوﺪﺟ -2 ﻪﻧﻮﻤﻧ ﺬﺧاياﺮﺑﺮﻳوﺎﺼﺗيورﺮﺑتﺎﺸﻳﺎﻣزآيﺎﻫ ﺎﻫﺮﺘﻣارﺎﭘ

ﺶﻳﺎﻣزآ

لﻮﻃﺖﺒﺴﻧهزاﺪﻧا ﻪﻴﺣﺎﻧضﺮﻋﻪﺑ

ﺐﻳﺮﺿهزاﺪﻧا ﻲﮕﺘﺴﺒﻤﻫ هﺮﻬﭼﺪﺻرد

هﺪﺷﻒﺸﻛ ﺮﻳوﺎﺼﺗداﺪﻌﺗ هﺮﻬﭼﺮﻴﻏﻲﻔﺸﻛ

1 0.2

0.58

75

5

2 0.4 0.58

75

5

3 0.6 0.58

75

3

4 0.8 0.58

68

2

5 1.0 0.58

60

1

8 ﺶﻳﺎﻣزآ ﺞﻳﺎﺘﻧ - ﻲﺑﺮﺠﺗ يﺎﻫ

ﻦﻳاﻲﻌﺳﻪﻛنآﻪﺑﻪﺟﻮﺗﺎﺑ ﺺﻴﺨﺸﺗياﺮﺑكﻻﺎﭼوﻚﺒﺳشورﻚﻳﻪﻳاراردﻪﻟﺎﻘﻣ

ﻪﺴﻳﺎﻘﻣزااﺬﻟدرادﺐﺳﺎﻨﻣﺖﺤﺻوﺖﻗدﺎﺑﻲﺷورﻪﻳاراﻪﺑﻪﺟﻮﺗوﺖﺳانﺎﺴﻧاهﺮﻬﭼ ﺘﻧﺎﺑ ﺖﺳاهﺪﺷيراددﻮﺧناﺮﮕﻳدﺞﻳﺎ شور .

ﻦﻳﻮـﻧيﺎـﻫ شورسﺎـﺳاﺮﺑﺮﺘـﺸﻴﺑ

يﺎـﻫ

رﺎﻴﺴﺑوهﺪﻴﭽﻴﭘ ﻲﻣﺎﻣشورﻲﻟوﺪﻨﺘﺴﻫﺺﻴﺨﺸﺗﻖﻴﻗد

ﻪﺑﺪﻧاﻮﺗ ـﻳناﻮﻨﻋ ﻪـﻠﺣﺮﻣﻚ

شورياﺮﺑنزوﻢﻛوكﻻﺎﭼ ﻠﺣﺮﻣردنآﺰﻛﺮﻤﺗﺮﺘﺸﻴﺑﻪﻛدﻮﺷهدﺎﻔﺘﺳاﺮﮕﻳديﺎﻫ

ﻪ ﺖﻴﻠﻣدﺮﺑرﺎﻛياﺮﺑﻲﻳﺰﺟيﺮﻴﮔدﺎﻳ وصﺎﺧيﺎﻫ

ﻲﺘﺳﻮﭘﺮﻳوﺎﺼﺗ ﺖﺳانﻮﮕﻤﻫ

. ﺞﻳﺎـﺘﻧ

ﺶﻳﺎﻣزآ ﻪﻧﻮﻤﻧيورﺮﺑﺎﻫ ﻫ

ﻪﻧﻮﻤﻧودراﺪﻧﺎﺘﺳاﺎ ﻳايﺎﻫ

نﺎﻴﺑﺶﺨﺑﻦﻳاردهﺪﺷﺬﺧاﻲﻧاﺮ

ﻲﻣ ددﺮﮔ يﺎﻫﺮﺘﻣارﺎﭘنﻮﻣزآياﺪﺘﺑارد . ﻣ

هزاﺪـﻧاﺮﻳوﺎـﺼﺗردﻢﺘـﺴﻴﺳزﺎﻴﻧدرﻮ يﺮـﻴﮔ

ﺖﺳاهﺪﻳدﺮﮔﺬﺧاهﺮﻬﭼﺺﻴﺨﺸﺗﺞﻳﺎﺘﻧﺲﭙﺳوهﺪﺷ .

8 - 1 ﺶﻳﺎﻣزآ تﺎﻤﻴﻈﻨﺗ - ﺎﻫ

هدادهﺎﮕﻳﺎﭘودزانﻮﻣزآياﺮﺑﺎﻣ يوﺎﺣﻲﻧاﺮﻳاﻲﮔداﻮﻧﺎﺧﺮﻳوﺎﺼﺗيا

ﻲﮕﻧرﺮﻳﻮﺼﺗ600

هﺮﻬﭼﺪﻨﭼ ﺑاﺎﺑصﺎﺧﺮﻳوﺎﺼﺗيارادويا هﺮـﻬﺑهﺮـﻬﭼتوﺎﻔﺘﻣﺖﻬﺟودﺎﻌ

هدﺮـﺑ ﻢـﻳا .

ﺖﻴﻠﻣياﺮﺑ ياراددراﺪﻧﺎﺘﺳاﺮﻳوﺎﺼﺗزاتوﺎﻔﺘﻣيﺎﻫ ﺪﻨﭼﻦﺘﺷادﺎﺑﻲﮕﻧرﺮﻳﻮﺼﺗ400

ﺮﻳﻮﺼﺗﻚﻳردنﺎﻣﺰﻤﻫهﺮﻬﭼ هﺮـﻬﺑ

ﺖـﺳاهﺪـﺷيرادﺮـﺑ .

هﺮـﻬﭼﺺﻴﺨـﺸﺗﻢﺘـﺴﻴﺳ

ﻬﻨﺸﻴﭘ ـ زاهدﺎﻔﺘﺳاﺎﺑيدﺎ MATLABR2014b

ـﻴﺷﺎﻣيورﺮﺑ ـ

ﻦ 2.4 GHZIntel

رﺎﺘﺧﺎﺳﺎﺑ Corei4 ﻦﺘﺷادو ﻪﻈﻓﺎﺣ 4 GB RAM ﺖﺤﺗ

Windows 7 هدﺎﻴﭘ

يزﺎﺳ

ﺖﺳاهﺪﺷنﻮﻣزآو .

8 - 2 ﻲﺑﺎﻳزرا يﺎﻫرﺎﻴﻌﻣ -

ﺖﻗديﺎﻫرﺎﻴﻌﻣﻲﺑﺎﻳزرازاﻢﺘﺴﻴﺳدﺎﻨﺘﺳادرﻮﻣﺞﻳﺎﺘﻧ ﻲﻧاﻮﺧاﺮﻓ،1

ﺖﻓﺎﻳزﺎﺑ)

2( رﺎﻴﻌﻣو

زﺎﻴﺘﻣا -

3F ﻪﺑ ﻂﺑاورحﺮﺷﻪﺑﻪﻛﺖﺳاهﺪﻣآﺖﺳد )

8

،( ) 9 و ( ) 10 ﺪﻨﺘﺴﻫ ( .

) 8 (

Pr ( )

ecision TP

TP FP

 

) 9 (

Re ( )

call TP

TP TN

 

) 10 (

2 Pr Re

(Pr Re )

ecision call F score

ecision call

 

 



ﻪﻛﻲﻳﺎﺟ ﻲـﻌﻗاوﺖـﺒﺜﻣ هﺮـﻬﭼ)

ﻲـﻣﻲﺑﺎـﻳزﺎﺑﻪـﻛﻲﻳﺎـﻫ ﺪﻧﻮـﺷ

( ﻲـﻌﻗاوﻲـﻔﻨﻣ،

هﺮﻬﭼ) زﺎﺑﻪﻛﻲﻳﺎﻫ ﻲﻤﻧﻲﺑﺎﻳ

ﺪﻧﻮﺷ بذﺎﻛﺖﺒﺜﻣو ( ﺮﻴﻏ)

هﺮﻬﭼ ﻫ هﺮﻬﭼناﻮﻨﻋﻪﺑﻪﻛﻲﻳﺎ

ﻲﻣﻲﺑﺎﻳزﺎﺑ ﺪﻧﻮﺷ زاﺪﻨﺗرﺎﺒﻋﺐﻴﺗﺮﺗﻪﺑ ( :

: : :

TP True Positive TN True Negative FP False Positive

8 - 3 جاﺮﺨﺘﺳا - ﺮﻳوﺎﺼﺗ يﺎﻫﺮﺘﻣارﺎﭘ

ﺶﻳﺎﻣزآﻲﺑﺮﺠﺗﺞﻳﺎﺘﻧزاهدﺎﻔﺘﺳاﺎﺑ

،ﺎﻫ راﺪﻘﻣﻦﻳﺮﺘﻬﺑ ﻲﮕﺘـﺴﺒﻤﻫﺐﻳﺮﺿياﺮﺑﻪﻨﻴﻤﻛ

ﺖﺳﺪﺑ0.58 ﺪﻣآ نآﻦﻤﺿ . ﻪـﻴﺣﺎﻧضﺮـﻋولﻮـﻃﺖﺒـﺴﻧياﺮـﺑﻪـﻨﻴﻬﺑراﺪـﻘﻣﻪـﻛ

هﺮﻬﭼ هدوﺪﺤﻣرديا ﺎﺗ0.6

1.2 ﻪـﺘﻓﺮﮔﺮـﻈﻧرد ﺖـﺳاهﺪـﺷ

. لواﺪـﺟﺞﻳﺎـﺘﻧ و1

2

يورﺮﺑﺶﻳﺎﻣزآزاﻞﺻﺎﺣ ﻞﻣﺎﺷﺮﻳﻮﺼﺗ200

ﺖـﺳاهﺮﻬﭼ300 .

ﻦـﻳازاهدﺎﻔﺘـﺳاﺎـﺑ

ﺖﺳاﻲﻬﻳﺪﺑ،ﺞﻳﺎﺘﻧ ﻪـﻴﺣﺎﻧضﺮـﻋﻪـﺑلﻮـﻃﺖﺒﺴﻧياﺮﺑﻪﻨﻴﻤﻛﻪﻨﻴﻬﺑراﺪﻘﻣﻪﻛ

0.6

ﻦـﻳاردﺖـﺳردﺎﻧﺺﻴﺨـﺸﺗخﺮﻧﻦﻳﺮﺘﻤﻛوﺖﺳردﺺﻴﺨﺸﺗخﺮﻧﻦﻳﺮﺗﻻﺎﺑاﺮﻳزﺖﺳا ﻲﻣخرراﺪﻘﻣ ﺪﻫد

ﺶﻳﺎﻣزآمﺎﺠﻧاﺎﺑ . اﺪﻘﻣﻪﺑﺎﺸﻣيﺎﻫ

ﻲﮕﺘﺴﺒﻤﻫﺐﻳﺮﺿﻪﻨﻴﻬﺑر 0.58

ﺖﻓﺎﻳ ﺷ ﺪ آمﺎﺠﻧاوﺞﻳﺎﺘﻧزاهدﺎﻔﺘﺳاﺎﺑ . يورﺮﺑﺶﻳﺎﻣز

ﺘﺨﻣﺮﻳﻮﺼﺗ200 ﺪـﺻرد،ﻒـﻠ

هﺮﻬﭼ ﻒﺸﻛيﺎﻫ هﺪﺷ

89 هﺪﻣآﺖﺳﺪﺑ% نﺎﺸﻧﻪﻛ

ﻪـﻛﺖـﺳاﻢﺘـﺴﻴﺳﺖـﻗدهﺪﻨﻫد

ﻳوﺎﺼﺗياﺮﺑ ﻲﻣﺖﻓارﺎﭼديدوﺪﺣﺎﺗﺶﻳريارادﺮ دﻮﺷ

رد . لﺮـﺘﻨﻛﻂﻳاﺮـﺷردﻞﺑﺎﻘﻣ

ﺪﺷرﺖﻗد،هﺮﻬﭼﺖﻴﻌﺿووﻪﻨﻴﻣزﺲﭘهﺪﺷ 98

ﻲﻣنﺎﺸﻧار % ﺪﻫد

. ﻢﺘـﺴﻴﺳﺖﻋﺮﺳ

ع اوﻲﻋراز ﻪﻣ آﺑ يدﺎ ردنﺎﺴﻧاهﺮﻬﭼيزﺎﺳرﺎﻜﺷآ لﺎﺘﻴﺠﻳدﺮﻳوﺎﺼﺗ

يدﺎﻋﻪﻟﺎﻘﻣ

ﻢﺘﺴﻴﺳﺎﺑﻪﺴﻳﺎﻘﻣرد ﻢﺘﻳرﻮﮕﻟازاﻪﻛﻪﺑﺎﺸﻣيﺎﻫ

يﺎﻫ ﻲﻣهدﺎﻔﺘﺳاﻲﺒﺼﻋﻪﻜﺒﺷ ﺪـﻨﻨﻛ

ﺖﺳاﺮﺘﺸﻴﺑ .

8 - 4 ﻢﺘﺴﻴﺳ ﻲﺑﺮﺠﺗ ﺞﻳﺎﺘﻧ -

ﻲﻣ ﺖﻗدناﻮﺗ ﻪﺑﻦﻴﻳﺎﭘرﺎﻴﺴﺑﺖﻓﺎﻳزﺎﺑﺎﺑﻻﺎﺑ ﺶﻫﺎـﻛﺎـﺑﻲـﻜﻳﺶﻳاﺰـﻓاﻪﻛدروآﺖﺳد

ﺖﺳاهاﺮﻤﻫيﺮﮕﻳد رﺎﻴﻌﻣ .

ﺎﺑوﺖﻗدرﺎﻴﻌﻣزاﻲﺒﻴﻛﺮﺗF ﺎـﺑرﺎﻛترﺪﻗﻪﻛﺖﺳاﺖﻓﺎﻳز

ﻪﻋﻮﻤﺠﻣ ﻲﻣنﺎﺸﻧارلدﺎﻌﺘﻣﺮﻴﻏيﺎﻫ ﻫد

ﻪﻛﺪ ﻲﻣنﺎﺸﻧارﺖﻴﻌﻧﺎﻣوﺖﻴﻌﻣﺎﺟ ﺪـﻫد

.

لوﺪﺟرد ﻪﻳاراشورﺞﻳﺎﺘﻧﻪﻛﺖﺳاﺺﺨﺸﻣ3

رﺎﻴـﺴﺑﻲﻧاﻮﺧاﺮﻓوﺖﻗدﺮﻈﻧزاهﺪﺷ

رﺎﻴﻌﻣوﺖﺳابﻮﺧ هﺪـﺷﻪـﻳاراشوربﻮـﺧﺎﺘﺒـﺴﻧﺖـﻴﻌﻣﺎﺟزانﺎﺸﻧلوﺪﺟردF

ﺖﺳا ﺶﻳﺎﻣزآ . نﺎﺸﻧﺎﻣﻒﻠﺘﺨﻣيﺎﻫ د

ﺺﻴﺨـﺸﺗدورﺮﺗﻻﺎﺑﺮﻳوﺎﺼﺗداﺪﻌﺗﻪﭼﺮﻫﻪﻛدا

ﻣﺶﻫﺎﻛهﺮﻬﭼﺖﺳرد ﻲ

ﺪﺑﺎﻳ ﻟاﺎـﺑارﻪﻟﺎﺴﻣﻦﻳاﻢﻴﺘﺴﻧاﻮﺗﻪﺘﺒﻟا . لﺮـﺘﻨﻛهﺮـﻬﭼيﻮـﮕ

ﻪﻧﻮﻤﻧﺬﺧاوﻢﻴﻨﻛ ﺖﺳاهدﺮﻛﻚﻤﻛرﺎﻴﺴﺑنآﺶﻫﺎﻛﻪﺑﻲﺘﺳﻮﭘيﺎﻫ

. ﻪـﻛﻢـﻳرادﺎﻨﺑ

ﻢﻴﻫدﺶﻫﺎﻛرﺎﻴﺴﺑارﺖﻴﺳﺎﺴﺣﻦﻳاﻲﻠﺤﻣيﺮﻴﮔدﺎﻳﺐﻴﻛﺮﺗﺎﺑهﺪﻨﻳآرد .

لوﺪﺟ -3 ﻒﻠﺘﺨﻣﻂﻳاﺮﺷردﺮﻳوﺎﺼﺗردهﺮﻬﭼﺺﻴﺨﺸﺗﺞﻳﺎﺘﻧ

ﺪﺸﻧلﺮﺘﻨﻛﻂﻳاﺮﺷ ه

هﺪﺷلﺮﺘﻨﻛﻂﻳاﺮﺷ رﺎﻴﻌﻣ

هﺮﻬﭼﺪﻨﭼ هﺮﻬﭼﻚﺗ هﺮﻬﭼﺪﻨﭼ هﺮﻬﭼﻚﺗ 0.978 0.975

0.986 Pecision 0.98

0.56 0.56 0.57

Recall 0.56

0.712 0.711 0.72 0.711 F-Score

9 ﻪﺠﻴﺘﻧ - يﺮﻴﮔ

ﻲﻣيدﺎﻬﻨﺸﻴﭘشور ﻪﺑﺪﻧاﻮﺗ

ﻊﻳﺮـﺳﻲﺘﻌﻨﺻﻢﺘﻳرﻮﮕﻟاﻚﻳناﻮﻨﻋ ﺺﻴﺨـﺸﺗﻖـﻴﻗدو

نﺎﺴﻧاهﺮﻬﭼرﺎﻛدﻮﺧ ﺖﺤﺗ

ﻂﻳاﺮﺷوﻲﻧﻮﻧﺎﻗﻲﻜﺷﺰﭘﺰﻛاﺮﻣﺪﻨﻧﺎﻣهﺪﺷلﺮﺘﻨﻛﻂﻳاﺮﺷ

لﺮﺘﻨﻛ ﻪﺑهﺪﺸﻧ ﻪﺑداﺮﻓاﺖﻳﻮﻫﻦﻴﻴﻌﺗرﻮﻈﻨﻣ دوررﺎـﻛ

. هﺮـﻬﭼيﺮﺛﻮـﻣﻮـﺤﻧﻪـﺑ يﺎـﻫ

دﻮﺟﻮﻣ ﻲﻣنﺎﺸﻧارﻲﮕﻧرﺮﻳوﺎﺼﺗرد وﺪﻫد

ﺖـﺳاﻻﺎـﺑﺖـﻗدياراد .

ﻪـﻛيدراﻮـﻣرد

ﺖﻗديارادﺪﺷﺎﺑﻪﻣﺎﻧرﺬﮔﺲﻜﻋﻂﻳاﺮﺷﻪﺑﻚﻳدﺰﻧﻲﻄﻳاﺮﺷردﺮﻳﻮﺼﺗ

% ﺖﺳا98 رد .

ﺲﭘﻪﻛيدراﻮﻣ ﻲﺣاﻮﻧﺎﺑﻪﺑﺎﺸﻣﮓﻧرياراديﺮﻳﻮﺼﺗﻪﻨﻴﻣز

ﻖﻓﻮﻣﺰﻴﻧﺪﺷﺎﺑﻲﺸﺷﻮﭘ

ﻞﻤﻋ ﻣ ﻲ ﻛ ﺪﻨ . ﻪـﺑﻚﻳدﺰﻧﺮﻳوﺎﺼﺗياﺮﺑهﺮﻬﭼيﻮﮕﻟاجاﺮﺨﺘﺳاترﻮﺻرد ﻢـﻫ

) ﺪـﻨﻧﺎﻣ

ﻠﻣﻚﻳ صﺎﺧﺖﻴ ﻲـﻣﺮﺘﻤﻛﻢﺘﺴﻴﺳيﺎﻄﺧﺪﺻرد (

دﻮـﺷ ﺮـﺑ . هﺮـﻬﭼﻒـﺸﻛيا يﺎـﻫ

ﻢﻴﻧ ﻲﻓﺎﻛخر هﺮﻬﭼيﻮﮕﻟاﺖﺳا ﺠﻳاخرﻢﻴﻧهﺮﻬﭼياﺮﺑيا

ودﺎ رد ﻮﮕﻟاﻖﻴﺒﻄﺗﻪﻠﺣﺮﻣ

هدﺎﻔﺘﺳانآزا دﻮﺷ

. ﻲـﻠﺤﻣيﺮﻴﮔدﺎـﻳيﺮﻴﮔرﺎﻜﺑﺎﺑﻪﻛﻢﻳرادنآﺮﺑﻲﻌﺳهﺪﻨﻳآياﺮﺑ

ﻲﻤﻛ ﻢﻴﻫﺎﻜﺑيدﺎﻬﻨﺸﻴﭘشورﺖﻴﺳﺎﺴﺣزا .

ﻊﺟاﺮﻣ

[1] P. Sinha, and et. al., "Face Recognition by Humans:

Nineteen Results All Computer Vision Researchers Should Know About," Proceedings of the IEEE, vol. 94, no. 11, pp.

1948-1962, 2006.

[2] K. Lander, and L. Chuang, "Why are Moving Faces Easier to Recognize," Visual Cognition, vol. 12, pp.

429–442, 2005.

[3] M. H. Yang, D. Keriegman, and N. Ahuja, "Detecting Faces in Images: A Survey," IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI), vol. 24, no. 1, pp. 34-58,

2002.

[4] V. Bruce, and A. W. Young, "Understanding Face Recognition," Br. J. Psychol., no. 77, pp. 305–327, 1986.

[5] R. Chellappa, C. Wilson, and S. Sirohey, "Complex Background Using Color Information and SGLD Matrices,"

Proc. First Int’l Workshop Automatic Face and Gesture Recognition, pp. 238-242, 1995.

[6] J. L. Crowley, and J. M. Bedrune, "Integration and Control of Reactive Visual Processes," Proc. Third European Conf. Computer Vision, no. 2, pp. 47-58, 1994.

[7] A. J. Calder, and et. al., "Configural Information in Facial Expression Perception," J. Exp. Psychol. Hum.

Percept. Perform., vol. 26, pp. 527–551, 2000.

[8] H. Wang, and S. F. Chang, "A Highly Efficient System for Automatic Face Region Detection in MPEG Video,"

IEEE Trans. Circuits and Systems for Video Technology, vol. 7, no. 4, pp. 615-628, 1997.

[9] J. Cai, A. Goshtasby, and C. Yu, "Detecting Human Faces in Color Images," Image and Vision Computing, vol.

18, no. 1, pp. 63-75, 1999.

[10]A. J. Calder, and A. W. Young, "Understanding the Recognition of Facial Identity and Facial Expression,"

Nature Rev. Neurosci., vol. 6, no. 8, pp. 641–651, 2005.

[11]Y. Gong, and M. Sakauchi, "Detection of Regions Matching Specified Chromatic Features," Computer Vision and Image Understanding, vol. 61, no. 2, pp. 263–269, 1995. [12]A. J. Colmenarez, and T. S. Huang, "Face Detection with Information-based Maximum Discrimination," Proc.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 782–787, 1997.

[13]J. L. Crowley, and F. Berard, "Multi-modal Tracking of Faces for Video Communications," Proc. Computer Vision and Pattern Recognition, Puerto Rico, pp. 640–645, June 1997.

[14]T. F. Cootes, and C. J. Taylor, "Locating Faces using Statistical Feature Detectors," Proc. Second International Conference on Automatic Face and Gesture Recognition, pp.

204–209, 1996.

[15]T. K. Leung, M. C. Burl, and P. Perona, "Finding Faces in Cluttered Scenes using Random Labeled Graph Matching," Proc. Fifth International Conference on Computer Vision, pp. 637–644, 1995.

[16]H. A. Rowley, S. Bluja, and T. Kanade, "Neural Network-based Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23–38, 1998.

[17]K.-K. Sung, and T. Poggio, "Example-based Learning for View-based Human Face Detection," IEEETrans. Pattern

[30 De of ﻲــﺳ ﺲﻧ ﺬـﺧ ﺮﻳ و

a.z ﻲـﺳ وهﺪ تﺎـ

شز ﺖ . ma

1 Pr

2 Re

3 F-

0]A. Goswam etection using

Current Engin ﺳﺪﻨﻬﻣﻪﺘــﺷرردا

قﻮﻓوزاﺮﻴﺷ ﺎـﺴﻴﻟ

ﺧاﺪﻫﺎـﺷهﺎﮕﺸﻧادز ﻳﻮﺼﺗشزادﺮﭘهدﺮﺒ

zarei1600@gm ﺳﺪﻨﻬﻣﻪﺘـﺷرردار ﺪﻧﺎـﺳرمﺎـﺠﻧاﻪـﺑﺮ ـﻋﻼﻃايروﺎﻨﻓوﺮﺗ زادﺮـﭘهدﺮﺒﻣﺎﻧﻪﻗﻼﻋ ﺖﺳاﺪﻨﻤﺷﻮﻫيزﺎﺳ ahabadi@shah

recision ecall -Score

هﺎﮕـﺸﻧاد،ﻲـﺳﺪﻨﻬ

mi, and A. D Skin Pixels a neering and T ردﻮــﺧﺲﻧﺎــﺴﻴﻟ

هﺎﮕﺸﻧادز رﻮﻧمﺎﻴﭘ زاﺮﺗﻮﻴﭙﻣﺎﻛيرﺎﻤﻌ ﺒﻣﺎﻧﻪﻗﻼﻋدرﻮﻣتﺎ ﺖ . زا : mail.com

ﻼﻴـﺼﺤﺗ ردﻮـﺧت ﺮﺗﻮﻴﭙﻣﺎـﻛيرﺎـﻤﻌﻣ ﺗﻮﻴﭙﻣﺎﻛﻲﺳﺪﻨﻬﻣه ﻼﻋدرﻮﻣتﺎﻘﻴﻘﺤﺗ .

ﻪﻴﺒﺷوﺪﻨﻤﺷﻮﻫ ﺳ

زا : hed.ac.ir

ﻬﻣﻲـﻨﻓهﺪﻜـﺸﻧاد

Datar, "Fronta and ANN," Int echnology, vo ﻲــﻋرازﺎــﺿﺮﻴﻋ

مﺮﻧﺮﺗﻮﻴﭙﻣﺎﻛ زاراﺰﻓا

ﻌﻣﻪﺘﺷررداردﻮﺧ ﺖﺳاهدﻮﻤﻧ ﺎﻘﻴﻘﺤﺗ .

ﺖﺳاﻦﻴﺷﺎﻣﻲﻳﺎﻨﻴﺑ ﺖﺳاترﺎﺒﻋنﺎﺸﻳ ﻦﻴﻣ ﻪﻣﻪﻟا يدﺎﺑآ ﺗ

ﺖﺨﺳقﺮ ﻣوراﺰـﻓا هوﺮﮔرﺎﻳدﺎﺘﺳانﻮﻨﻛ

ﺖﺳاﺪﻫﺎﺷهﺎﮕﺸﻧا ﻞﻘﻧوﻞﻤﺣ،ﺮﻳﻮﺼ

ﺖﺳاترﺎﺒﻋنﺎﺸﻳ

ﻪﻟ   :

1395

1395

/ 06 / 1395

ﻦﻴﻣا ﻪﻣﻪﻟا يدﺎﺑآ

،

al Enhanced ternational Jou ol. 6, no. 1, 20 ﻋ

ﻛ ﺧ ﻧ ﺑ

ﺖﺴﭘ ﻲﻜﻴﻧوﺮﺘﻜﻟا ﻳا

ﻣا ﺮﺑ ﻛا د ﺼﺗ

ﺖﺴﭘ ﻳاﻲﻜﻴﻧوﺮﺘﻜﻟا

ﺎﻘﻣﻲﺳرﺮﺑتﺎﻋﻼ لﺎﺳراﺦ : 11 / 05 / 5

حﻼﺻاﺦ : 31 / 05 / 5

نﺪﺷلﻮﺒﻗﺦ :

25 /

ﻂﺒﺗﺮﻣهﺪﻨﺴ ﺮﺘﻛد :

ناﺮﻳا،ناﺮﻬﺗ،ﺪﻫ .

Ana 1998 [18]

SGL Scen 1996 [19]

Imag 65-7 [20]

of H Con 514–

[21]

for Dete Con Verm [22]

Dete 713–

[23]

Fuzz Inter 1995 [24]

Colo Prin 165–

[25]

Face Inter Reco [26]

"Fac Feat Elec [27]

in C Aca 4, pp [28]

Reco Netw [29]

Dete Con 2010 Face

urnal 016.

سردآ

سردآ

ﻼﻃا ﺦﻳرﺎﺗ ﺦﻳرﺎﺗ ﺦﻳرﺎﺗ ﺴﻳﻮﻧ ﻫﺎﺷ

alysis and Mac 8.

Y. Dai, and LD and its A ne," Pattern R 6.

L. Xu, and ges," Image a 74, 1999.

K. C. Yow, Human face lo nference on C

–518, 1995.

K. C. Yow, a Perceptual G ection," Proc nference on A mont, USA, 1

K. C. Yow, a ection," Imag –735, 1997.

Q. Chen, H.

zy Pattern rnational Con 5.

Y. Miyake, or Correction nting," Journal –169, 1990.

K. Sobottka, es in Color

rnational Con ognition, pp. 2 A. Verma, S ce Detection u ture," 3rd In ctronics & Vis

F. Alizadeh, Color Images demy of Scien p. 366-372, 20 K. N. Lu, ognition usin works Transac

T. Talele, S ection using nference in C

0.

chine Intellige

Y. Nakano, "

Applications in Recognition, v

d et. al., "Se and Vision C

and R. Cipol ocation," Proc Computer Vi

and R. Cipolla Grouping of

ceedings of Automatic Fa

996.

and R. Cipolla ge and Visio

Wu, and M.

Matching,"

nference on C

and et. al., "

n from Televi l of Imaging T

and I. Pitas, "

Images," P nference on A 236–241, 199 S. A. Raj, A.

using Skin Co nternational sion (ICIEV),

S. Nalousi, an s using Colo nce Engineeri 011.

and A. N ng LDA-base ction, vol. 14, . Kadam, an Adaboost,"

omputational

ence, vol. 20,

"Face-texture n Face Detec vol. 29, no. 6,

egmentation Computing, vo

lla, "Finding I ceedings of th ision, Singap

a, "A Probabi f Features fo f the Secon ace and Gestu

a, "Feature-ba on Computin

Yachida, "Fa Proceedings omputer Visio

"Facial Patter ision Picture Technology, v

"Segmentation Proceedings

Automatic Fa 96.

. Midya, and olor Modelin Conference pp. 1-6, 2014 nd C. Savari, or Features o

ng and Techn

N. Venetsanop d Algorithms , no. 1, pp. 19 nd A. Tikare, IJCA Proc.

Intelligence

no. 1, pp. 39–

Model based ction in a Co , pp. 1007–10

of Skin Can ol. 17, no. 1,

Initial Estimat he Second As pore, no. 3,

ilistic Framew or Human F nd Internatio ure Recognit

ased Human F ng, no. 15,

ace Detection of the F on, pp. 591–5

rn Detection and Newspa vol. 16, no. 5,

n and Tracking of the Sec ace and Gest

d J. Chakrabo g and Geome on Informat 4.

, "Face Detect of Skin," Wo nology, vol. 5,

poulos, "F s," IEEE Neu

5–200, 2013.

"Efficient F on Internatio (ICCIA’12),

–51,

d on olor 017,

ncer pp.

tion sian pp.

work Face onal ion,

Face pp.

n by Fifth 596,

and aper pp.

g of ond ture

orty, etric

tics,

tion orld no.

Face ural

Face onal 10,

2

Human Face Recognition in Digital Images

Alireza Zarei Aminollah Mahabadi

Department of Electrical Engineering, Shahed University, Tehran, Iran

ABSTRACT

We present a light rapid and accurate method for detecting human faces in color images to constraint on the same nationality. The proposed learning method is different from the classical methods and developed via simple and speed learning to apply on accurate detection of human nationality. The method by using skin model information in color images can decrease the time of authentication without any dependency on lighting, background color, face status, face rotation, image dimension, resolution and imaging condition. The face recognition precision is 98% and 97%rate under the controlled and uncontrolled conditions, respectively. Our suggesting method can be applied as an accurate and rapid light automatic face recognition algorithm in an authentication system.

Keywords: Image Processing, Human Face Recognition, Skin Color, Skin Model.

The CSI Journal on Computing Science and Information Technology Vol. 14, No. 1, 2016