ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻘﻴﺎﺱ ﺍﻟﺤﻴﻮﻱ ﺍﻟﻤﺒﻨﻲ ﻋﻠﻰ ﺑﺼﻤﺔ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻘﻴﺎﺱ ﺍﻟﺤﻴﻮﻱ ﺍﻟﻤﺒﻨﻲ ﻋﻠﻰ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ) (Palmprint ﻣﻘﺪﻡ ﻣﻦ ﻗﺒﻞ: ﺑﺘﻮﻝ ﺍﻟﺤﻮﺍﻧﻲ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ • • • ﺍﻟﻤﺤﺘﻮﻳﺎﺕ ﺍﻟﻠﻤﺤﺔ ﺍﻟﻌﺎﻣﺔ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺗﺼﻨﻴﻒ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺍﻟﺘﻌﺮﻑ ﻋﻠﻰ ﺍﻻﺷﺨﺎﺹ ﻣﻦ ﺧﻼﻝ ﺍﻟﻴﺪ ﻭﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻣﺰﺍﻳﺎ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺍﻟﺘﻲ ﺗﺘﻔﻮﻑ ﺑﻬﺎ ﻋﻠﻰ ﺍﻟﻘﻴﺎﺱ ﺍﻟﺤﻴﻮﻱ ﻟﻠﻴﺪ ﻣﻼﻣﺢ ﻣﺨﺘﻠﻔﺔ ﻟﺒﻨﻴﺔ ﺍﻟﻜﻒ ﺗﻄﺒﻴﻘﺎﺕ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻓﻲ ﺍﻟﺤﻴﺎﺓ ﺍﻟﻌﻤﻠﻴﺔ ﻣﺨﻄﻂ ﺍﻟﻤﺮﺍﺣﻞ ﺍﻻﺳﺎﺳﻴﺔ ﻓﻲ ﺍﻟﺘﻌﺮﻑ ﻋﻠﻰ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺍﻟﺤﺼﻮﻝ ﻋﻠﻰ ﺻﻮﺭﺓ ﺭﺍﺣﺔ ﺍﻟﻜﻒ) (Palmprint Acquisition ﺍﻟﻤﻌﺎﻟﺠﺔ ﺍﻻﻭﻟﻴﺔ ) (Preprocessing ﺍﺳﺘﺨﺮﺍﺝ ﺍﻟﺨﺼﺎﺋﺺ ) (Feature Extraction ﻣﺮﺣﻠﺔ ﺍﻟﻤﻄﺎﺑﻘﺔ ) (Maching

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﻣﺤﺘﻮﻳﺎﺕ ﻭﺭﻗﺔ ﺍﻟﺒﺤﺚ ﻟﻤﺤﺔ ﻋﺎﻣﺔ ﺣﻮﻝ ﻣﻮﺿﻮﻉ ﺍﻟﻮﺭﻗﺔ ﺍﻟﺒﺤﺜﻴﺔ ﻣﺮﺣﻠﺔ ﺍﻟﺤﺼﻮﻝ ﻋﻠﻰ ﺻﻮﺭﺓ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺍﻟﻤﻌﺎﻟﺠﺔ ﺍﻻﻭﻟﻴﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺼﻮﺭﺓ ﺗﻤﺜﻴﻞ ﺍﻟﺨﺼﺎﺋﺺ ﻟﺒﺼﻤﺔ ﺍﻟﻜﻒ ﻣﺮﺣﻠﺔ ﺍﻟﻤﻄﺎﺑﻘﺔ ﻭﺗﺤﺪﻳﺪ ﺍﻟﻬﻮﻳﺔ ﺍﻟﻨﺘﺎﺋﺞ ﺍﻟﺘﺠﺮﻳﺒﻴﺔ ﺍﻟﻤﺮﺍﺟﻊ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ overveiw ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺗﺆﺨﺬ ﻟﻠﻤﻨﻄﻘﺔ ﺍﻟﺪﺍﺧﻠﻴﺔ ﻣﺎ ﺑﻴﻦ ﻧﻬﺎﻳﺔ ﺍﻻﺻﺎﺑﻊ ﻭﺑﺪﺍﻳﺔ ﻣﻌﺼﻢ ﺍﻟﻴﺪ. ﻭﻳﺘﻢ ﺍﻟﺤﺼﻮﻝ ﻋﻠﻰ ﺻﻮﺭﺓ ﺭﺍﺣﺔ ﺍﻟﻜﻒ ﺇﻣﺎ ﺑﺸﻜﻞ ﻣﺒﺎﺷﺮ) (online ﻣﻦ ﺧﻼﻝ ﺍﻻﺟﻬﺰﺓ ﺍﻻﻟﻜﺘﺮﻭﻧﻴﺔ)ﺃﻲ ﻋﻦ ﻃﺮﻳﻖ ﺍﻟﻤﺎﺳﺢ ﺍﻟﻀﻮﺋﻲ ﺃﻮ (CCD ﺍﻭ ﺑﺸﻜﻞ ﻏﻴﺮ ﻣﺒﺎﺷﺮ ) (offline ﺣﻴﺚ ﻳﺘﻢ ﺃﺨﺬ ﺻﻮﺭﺓ ﻣﻦ ﺧﻼﻝ ﺍﻟﺤﺒﺮ ﻭﺍﻟﻮﺭﻕ.

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺗﺼﻨﻴﻒ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻭﺗﺼﻨﻒ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻣﻦ ﺿﻤﻦ ﺍﻟﺨﺼﺎﺋﺺ ﺍﻟﻔﺴﻴﻮﻟﻮﺟﻴﺔ ﺍﻟﺘﻲ ﻳﻤﻜﻦ ﺍﺳﺘﺨﺪﺍﻣﻬﺎ ﻟﻠﺘﻤﻴﻴﺰ ﺑﻴﻦ ﺍﻷﻔﺮﺍﺩ. ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﺴﻠﻮﻛﻴﺔ ﺍﻟﺘﻮﻗﻴﻊ ﺍﻟﺼﻮﺕ ﺍﻟﻔﺴﻴﻮﻟﻮﺟﻴﺔ ﻫﻨﺪﺳﺔ ﺍﻟﻴﺪ ﺍﻟﻘﺰﺣﻴﺔ ﺭﺍﺣﺔ ﺍﻟﻜﻒ ﺍﻟﻮﺟﻪ

ﺍﻟﺘﻌﺮﻑ ﻋﻠﻰ ﺍﻷﺸﺨﺎﺹ ﻣﻦ ﺧﻼﻝ ﺍﻟﻴﺪ ﻭﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺑﺼﻤﺔ ﺧﻼﻝ ﻣﻦ ﺍﻟﺘﻌﺮﻑ ﺍﻟﻜﻒ ------------ ﻳﺮﻛﺰ ﻋﻠﻰ ﺍﻟﺴﻄﺢ ﺍﻟﺪﺍﺧﻠﻲ ﻣﻦ ﺟﻬﺔ، ﻧﻤﻄﻬﺎ ﻣﻦ ﺧﻄﻮﻁ ﻭﺷﻜﻞ ﺳﻄﺤﻪ ﺍﻟﺘﻌﺮﻑ ﻣﻦ ﺧﻼﻝ ﺷﻜﻞ ﺍﻟﻴﺪ ----------- ﻳﺮﻛﺰ ﻋﻠﻰ ﺍﺑﻌﺎﺩ ﺍﻟﻴﺪ ﻭ ﺷﻜﻠﻬﺎ )ﻛﺎﻟﺤﺠﻢ ﻭ ﺍﻟﻄﻮﻝ(

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﻣﺰﺍﻳﺎ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺍﻟﺘﻲ ﺗﺘﻔﻮﻑ ﺑﻬﺎ ﻋﻠﻰ ﺍﻟﻘﻴﺎﺱ ﺍﻟﺤﻴﻮﻱ ﻟﻠﻴﺪ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻟﺪﻳﻬﺎ ﺑﻌﺾ ﺍﻟﻤﺰﺍﻳﺎ ﺗﺘﻔﻮﻕ ﺑﻬﺎ ﻋﻠﻰ ﺍﻟﺘﻘﻨﻴﺎﺕ ﺍﻟﺒﻴﻮﻣﺘﺮﻳﺓﺎﻻﺧﺮﻯ ﻟﻠﻴﺪ، ﻣﺜﻞ ﺑﺼﻤﺎﺕ ﺍﻷﺼﺎﺑﻊ ﻭ ﻫﻨﺪﺳﺔ ﺍﻟﻴﺪ ، ﻭﻣﻦ ﺍﻫﻤﻬﺎ : ﺭﺍﺣﺔ ﺍﻟﻜﻒ ﻛﺒﻴﺮﺓ ﺍﻟﺤﺠﻢ. ﺗﺤﺘﻮﻱ ﻋﻠﻰ ﻣﻴﺰﺍﺕ ﻭﻓﻴﺮﺓ ﻣﻦ ﻣﺴﺘﻮﻳﺎﺕ ﻣﺨﺘﻠﻔﺔ ، ﻣﺜﻞ ﺍﻟﺘﺠﺎﻋﻴﺪ ﻭﺍﻟﺨﻄﻮﻁ ﺍﻟﻜﻒ، ﻭﺍﻟﻤﻠﻤﺲ ﺍﻟﺦ. . . ﺗﺰﻭﻳﺮ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺃﻜﺜﺮ ﺻﻌﻮﺑﺔ ، ﻭﺫﻟﻚ ﻟﻸﺴﺒﺎﺏ ﺍﻟﺘﺎﻟﻴﺔ: - ﻧﺴﻴﺞ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺃﻜﺜﺮ ﺗﻌﻘﻴﺪﺍ. - ﻣﻘﺎﺭﻧﺔ ﻣﻊ ﺍﻷﺼﺎﺑﻊ، ﺍﻟﻜﻒ ﻫﻲ ﺃﻜﺜﺮ ﻋﺮﺿﺔ ﻟﻠﺘﻠﻒ ﻭ ﺍﻷﻮﺳﺎﺥ ﻟﺬﺍ ﻳﺼﻌﺐ ﺗﺰﻭﻳﺮﻫﺎ. ] 2 [

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺧﺼﺎﺋﺺ ﻣﺨﺘﻠﻔﺔ ﻓﻲ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻋﺮﺽ ﺍﻟﻜﻒ ﻃﻮﻝ ﺍﻟﻜﻒ ﺧﻄﻮﻁ ﺍﻟﻜﻒ ﺗﺠﺎﻋﻴﺪ ﺑﺎﻃﻦ ﺍﻟﻜﻒ ﺗﻼﻝ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻧﺴﻴﺞ ﻭ ﺗﻔﺼﻴﻼﺕ ﻧﻘﺎﻁ ﺍﻟﺪﻟﺘﺎ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺗﻄﺒﻴﻘﺎﺕ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻓﻲ ﺍﻟﺤﻴﺎﺓ ﺍﻟﻌﻤﻠﻴﺔ ﺍﻟﺘﻄﺒﻴﻘﺎﺕ ﺍﻟﺠﻨﺎﺋﻴﺔ ﺗﻄﺒﻴﻘﺎﺕ ﺍﻟﻄﺐ ﺍﻟﺸﺮﻋﻲ ﺍﻟﺘﻄﺒﻴﻘﺎﺕ ﺍﻟﺘﺠﺎﺭﻳﺔ ﻣﺴﺮﺡ ﺍﻟﺠﺮﻳﻤﺔ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﻣﺨﻄﻂ ﺍﻟﻤﺮﺍﺣﻞ ﺍﻻﺳﺎﺳﻴﺔ ﻓﻲ ﺍﻟﺘﻌﺮﻑ ﻋﻠﻰ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻣﺮﺣﻠﺔ ﺍﻟﺘﺼﻨﻴﻒ ﺍﺳﺘﺨﺮﺍﺝ ﺍﻟﺨﺼﺎﺋﺺ ﺍﻟﻤﻌﺎﻟﺠﺔ ﺍﻻﻭﻟﻴﺔ ﻟﻠﺼﻮﺭﺓ ﺍﺳﺘﺤﺼﺎﻝ ﺍﻟﺼﻮﺭﺓ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﺤﺼﻮﻝ ﻋﻠﻰ ﺻﻮﺭﺓ ﺭﺍﺣﺔ ﺍﻟﻜﻒ ﻳﺘﻢ ﺍﻟﺘﻘﺎﻁ ﺻﻮﺭﺓ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻣﻦ ﺧﻼﻝ ﻣﺎﺳﺢ ﺿﻮﺋﻲ ﺃﻮ ﻛﺎﻣﻴﺮﺍ ﺑﺼﻤﺔ ﺍﻟﻜﻒ. • ﻳﻮﺟﺪ ﺃﺮﺑﻌﺔ ﺃﻨﻮﺍﻉ ﻣﺨﺘﻠﻔﺔ ﻣﻦ ﺃﺠﻬﺰﺓ ﺍﻻﺳﺘﺸﻌﺎﺭ ﻟﺠﻤﻊ ﺍﻟﺼﻮﺭ 1ﺍﻟﻜﺎﻣﻴﺮﺍﺕ ﺍﻟﺮﻗﻤﻴﺔ 2ﺍﻟﻤﺎﺳﺤﺎﺕ ﺍﻟﻀﻮﺋﻴﺔ ﺍﻟﺮﻗﻤﻴﺔ 3ﻛﺎﻣﻴﺮﺍﺕ ﺍﻟﻔﻴﺪﻳﻮ 4ﺍﻟﻤﺎﺳﺤﺎﺕ ﺍﻟﻀﻮﺋﻴﺔ ﺑﺎﺳﺘﺨﺪﺍﻡ ﺟﻬﺎﺯ- CCD

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﻌﺎﻟﺠﺔ ﺍﻻﻭﻟﻴﺔ ) (Preprocessing ﺗﺤﻮﻳﻞ ﺻﻮﺭﺓ ﻣﻦ RGB ﺇﻟﻰ ﺍﻟﺮﻣﺎﺩﻱ ﻗﺺ ﻣﻨﻄﻘﺔ ﺍﻟﻜﻒ ﻓﻘﻂ ﻣﻦ ﺍﻟﻴﺪ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﺮﺍﺝ ﺍﻟﺨﺼﺎﺋﺺ ) Feature Extraction ﻭﻟﻘﺪ ﺗﻢ ﺍﻻﻋﺘﻤﺎﺩ ﻋﻠﻰ ﻋﺪﺓ ﻃﺮﻕ ﻻﺳﺘﺨﺮﺍﺝ ﻣﻴﺰﺍﺕ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺍﻟﻤﺨﺘﻠﻔﺔ ﻭﺍﻟﺘﻲ ﺗﺼﻨﻒ ﺇﻟﻰ ﺛﻼﺙ ﻓﺌﺎﺕ : ﺍﻟﺨﻄﻮﻁ ﺍﻷﺴﺎﺳﻴﺔ line based ﻣﻈﻬﺮ ﺍﻟﻜﻒ appearance-based ﻧﺴﻴﺞ ﺍﻟﻜﻒ texture-based

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺨﺼﺎﺋﺺ ﻋﺪﺓ ﻃﺮﻕ ﺗﺴﺘﺨﺪﻡ ﺍﻟﻤﺒﺪﺃ ﺍﻟﺬﻱ ﻳﻌﺘﻤﺪ ﻋﻠﻰ ﺍﻟﺨﻄﻮﻁ ﺍﻻﺳﺎﺳﻴﺔ ﻟﺒﺼﻤﺔ ﺍﻟﻜﻒ ﺑﺎﺳﺘﺨﺪﺍﻡ ﺃﺴﺎﻟﻴﺐ ﺍﻟﻜﺸﻒ ﻋﻦ : ﺍﻟﺤﻮﺍﻑ ﻣﺜﻞ -Sobel operator ([4]; [5] ) -morphological operator [6] -edge map [7] -modified radon transform [8]. -Other researchers implemented their own edge detection algorithms to extract the line patterns ([9]; [10]; [8]).

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺨﺼﺎﺋﺺ ﺍﻟﻨﻬﺞ ﺍﻟﻘﺎﺋﻢ ﻋﻠﻰ ﺍﻟﻤﻈﻬﺮ ﻫﻮ ﺃﻜﺜﺮ ﻭﺿﻮﺣﺎ ﻛﻤﺎ ﻳﻌﺎﻣﻞ ﺍﻟﺼﻮﺭﺓ ، ﺍﻟﻄﺮﻕ ﺍﻟﺸﺎﺋﻌﺔ ﻫﻲ : ﺗﺤﻠﻴﻞ ﺍﻟﻤﻜﻮﻥ ﺍﻟﺮﺋﻴﺴﻲ principal component analysis )]21[ ; ]11[( ) (PCA ﺗﺤﻠﻴﻞ ﺍﻟﺘﻤﺎﻳﺰ . )]21[( ) linear discriminant analysis (LDA ﺗﺤﻠﻴﻞ ﻋﻨﺼﺮ ﻣﺴﺘﻘﻞ independent component analysis . )]31[( ) (ICA ﻃﻮﺭ ﺑﻌﺾ ﺍﻟﺒﺎﺣﺜﻮﻥ ﺧﻮﺍﺭﺯﻣﻴﺎﺕ ﺧﺎﺻﺔ ﺑﻬﻢ ﻟﺘﺤﻠﻴﻞ ﻇﻬﻮﺭ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ) ] 41 [ (.

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺨﺼﺎﺋﺺ ﺍﻟﻨﻬﺞ ﺍﻟﻘﺎﺋﻢ ﻋﻠﻰ ﺗﺤﻠﻴﻞ ﺍﻟﻨﺴﻴﺞ ﻟﺮﺍﺣﺔ ﺍﻟﻜﻒ ، ﻭﻣﻦ ﺍﻟﻄﺮﻕ : ﺍﻟﺸﺌﻌﺔ ﻓﻲ ﻫﺬﺍ ﺍﻟﻨﻬﺞ -Law’s convolution masks. [15] -Gabar filter. -Fourier Transform. Among the methods tested, 2 -D Gabor filter has been shown to provide engaging result ([16]) - Ordinal measure ([17])

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﻄﺎﺑﻘﺔ ﻭﺍﻟﺘﺼﻨﻴﻒ ﺑﺎﻻﻋﺘﻤﺎﺩ ﻋﻠﻰ ﺃﻨﻮﺍﻉ ﻣﻦ ﺍﻟﻤﻴﺰﺍﺕ ﺍﻟﻤﺴﺘﺨﺮﺟﺔ، ﻭﺍﺳﺘﺨﺪﻣﺖ ﻣﺠﻤﻮﻋﺔ ﻣﺘﻨﻮﻋﺔ ﻣﻦ ﺗﻘﻨﻴﺎﺕ ﺍﻟﻤﻄﺎﺑﻘﺔ ﻟﻠﻤﻘﺎﺭﻧﺔ ﺑﻴﻦ ﺍﻟﺼﻮﺭ ﺍﻟﻤﺪﺧﻠﺔ ، ﻭﻫﻨﺎﻙ ﻧﻬﺠﻴﻦ ﺗﻢ ﺍﺗﺒﺎﻋﻬﻤﺎ : 1ﺍﻟﻤﻄﺎﺑﻘﺔ ﺍﻟﻘﺎﺋﻤﺔ ﻋﻠﻰ ﻫﻨﺪﺳﺔ ﺍﻟﻜﻒ geometry-based : matching ﻣﻘﺎﺭﻧﺔ ﺑﺎﺳﺘﺨﺪﺍﻡ ﺍﻟﻨﻘﺎﻁ ﻭﺧﻄﻮﻁ ﺍﻟﻤﻴﺰﺍﺕ] 71 [. 2ﺍﻟﻤﻄﺎﺑﻘﺔ ﺍﻟﺘﻲ ﺗﺴﺘﻨﺪ ﻋﻠﻰ ﺍﻟﻤﻴﺰﺓ feature-based : matching ﻳﻌﻤﻞ ﺑﺸﻜﻞ ﺟﻴﺪ ﻟﻠﻨﻬﺞ ﺍﻟﻘﺎﺋﻢ ﻋﻠﻰ ﺍﻟﻤﻈﻬﺮ ﻭ ﺍﻟﻘﺎﺋﻢ ﻋﻠﻰ ﺍﻟﻨﺴﻴﺞ ] 81[

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﺒﻴﺎﻧﺎﺕ ﺍﻟﻤﺴﺘﺨﺪﻣﺔ ﻓﻲ ﺍﻟﻮﺭﻗﺔ ﺍﻟﺒﺤﺜﻴﺔ ﻭﺗﺸﻤﻞ ﻋﻤﻠﻴﺔ ﺟﻤﻊ ﺍﻟﺒﻴﺎﻧﺎﺕ ﺃﺮﺑﻊ ﺧﻄﻮﺍﺕ : ) 1 ( ﺍﺳﺘﺨﺪﻡ ﺍﻟﺒﺎﺣﺚ 005 ﺷﺨﺺ ﻣﻦ ﻛﻼ ﺍﻟﺠﻨﺴﻴﻦ ﻭﻣﻦ ﺟﻤﻴﻊ ﺍﻷﻌﻤﺎﺭ. ) 2 ( ﺟﻤﻊ ﺳﺘﺔ ﻋﻴﻨﺎﺕ ﻟﺮﺍﺣﺔ ﺍﻟﻜﻒ ﻟﻜﻞ ﺷﺨﺺ ) 3 ( ﺍﺧﺘﻴﺎﺭ ﻋﺸﻮﺍﺋﻴﺎ ﻭﺍﺣﺪﺓ ﻣﻦ ﺍﻟﻌﻴﻨﺎﺕ ﺍﻟﺴﺖ ﻹﻋﺪﺍﺩ ﻗﺎﻋﺪﺓ ﺍﻟﺒﻴﺎﻧﺎﺕ، ﺍﻟﺘﻲ ﺍﺻﺒﺢ ﻟﺪﻳﻬﺎ 005 ﻧﻤﻮﺫﺝ) (training ) 4 ( ﺍﺳﺘﺨﺪﺍﻡ 0052 ﻋﻴﻨﺔ ﻋﻠﻰ ﺃﻨﻬﺎ ﻣﺠﻤﻮﻋﺔ ﺍﻻﺧﺘﺒﺎﺭ). (testing

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ : ﺍﺳﻢ ﺍﻟﺒﺤﺚ PALMPRINT BY FOURIER TRANSFORM

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﺴﺘﺨﺪﻣﺔ ﻓﻲ ﺍﻟﺒﺤﺚ ﺍﻟﺒﻴﺎﻧﺎﺕ ﺟﻤﻴﻊ ﺍﻟﻌﻴﻨﺎﺕ ﻛﺎﻧﺖ ﻟﻠﻴﺪ ﺍﻟﻴﻤﻨﻰ ، ﻭ ﺍﻟﺘﻘﻄﺖ ﻣﻦ ﻗﺒﻞ ﺟﻬﺎﺯ ﺑﺼﻤﺔ ﺍﻟﻜﻒ، ﻭﺍﻟﺘﻲ ﻳﻤﻜﻦ ﺃﻦ ﺗﻀﻊ ﺣﺪﺍ ﻟﺪﻭﺭﺍﻥ ﺍﻟﻴﺪ ﻟﻠﺤﺼﻮﻝ ﻋﻠﻰ ﺻﻮﺭﺓ ﺍﻟﻜﻒ ﺑﺎﺗﺠﺎﻩ ﺛﺎﺑﺖ ﻭﻣﺤﺪﺩ ﺍﻟﻌﻴﻨﺎﺕ ﺍﻟﻤﺎﺧﻮﺫﺓ ﻟﺮﺍﺣﺔ ﺍﻟﻴﺪ ﻛﺎﻧﺖ ﺟﻤﻴﻌﻬﺎ ﺑﺤﺠﻢ 042 × 023

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﻌﺎﻟﺠﺔ ﺍﻻﻭﻟﻴﺔ ﻟﺒﺼﻤﺔ ﺍﻟﻜﻒ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﻌﺎﻟﺠﺔ ﺍﻻﻭﻟﻴﺔ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﻌﺎﻟﺠﺔ ﺍﻻﻭﻟﻴﺔ (a)Three sample from the same palm with different directions and locations (b) Alignment results (c) The extracted subimages

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺨﺼﺎﺋﺺ ﺑﺎﺳﺘﺨﺪﺍﻡ Fourier Transform ﻳﺸﻤﻞ ﺍﻟﺘﻤﺜﻴﻞ ﺍﻟﺘﺤﻮﻳﻞ ﺍﻟﻰ Fourier transform ﻭﺍﻟﺘﺤﻮﻳﻞ ﺍﻟﻌﻜﺴﻲ. ﻋﻤﻠﻴﺔ ﺍﻟﺘﺤﻮﻳﻞ ﺗﺸﻤﻞ ﺍﻟﺘﺤﻮﻳﻞ ﻣﻦ ﺍﻟﻤﺠﺎﻝ ﺍﻟﻤﻜﺎﻧﻲ spatial domain ﺍﻟﻰ ﺍﻟﻤﺠﺎﻝ ﺍﻟﺘﺮﺩﺩﻱ frequency domain

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺨﺼﺎﺋﺺ ‹ ﺗﻢ ﺍﺳﺘﺨﺪﺍﻡ ﻣﺮﺷﺢ ﺍﻟﺘﺮﺩﺩﺍﺕ ﺍﻟﻌﺎﻟﻴﺔ ) (high pass filter ﻟﺪﻋﻢ ﺧﻄﻮﻁ ﺍﻟﺤﺪﻭﺩ ﺍﻟﻤﻮﺟﻮﺩﺓ ﻓﻲ ﺭﺍﺣﺔ ﺍﻟﻜﻒ ‹ﺗﻢ ﺍﺳﺘﺨﺪﺍﻡ ﻣﺮﺷﺢ ﺍﻟﺘﺮﺩﺩﺍﺕ ﺍﻟﻤﺘﺪﻧﻴﺔ ) (low pass ﬁlter ﻟﺘﺤﺴﻴﻦ ﺍﻟﺼﻮﺭﺓ ﺍﻟﻤﺪﺧﻠﺔ.

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺨﺼﺎﺋﺺ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺨﺼﺎﺋﺺ ﺗﺤﻮﻳﻞ ﻓﻮﺭﻳﻴﻪ ) (Foureier transform ﻳﻤﻜﻦ ﺍﺳﺘﺨﺪﺍﻣﻪ ﻓﻲ ﺍﺳﺘﺨﺮﺍﺝ ﺍﻟﺨﺼﺎﺋﺺ ﻟﺒﺼﻤﺔ ﺍﻟﻜﻒ. ﻫﺬﺍ ﻷﻦ ﻫﻨﺎﻙ ﺗﻮﺟﺪ ﺑﻌﺾ ﺍﻟﻨﻘﺎﻁ ﺍﻟﻤﺘﻘﺎﺑﻠﺔ ﻓﻲ ﺍﺳﺘﺨﺮﺍﺝ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻟﻠﺼﻮﺭﺓ ﺫﺍﺕ ﺍﻟﻨﻄﺎﻕ ﺍﻟﻤﻜﺎﻧﻲ ) (spatial domain ﻭ ﺗﻠﻚ ﺍﻟﺼﻮﺭﺓ ﺫﺍﺕ ﺍﻟﻨﻄﺎﻕ ﺍﻟﺘﺮﺩﺩﻱ ) . (frequency domain ﺣﻴﺚ ﺍﻥ : ﺍﻟﺘﺠﺎﻋﻴﺪ ﺍﻟﻘﻮﻳﺔ ﻭﺍﻟﻤﻀﻐﻮﻃﺔ ﻛﺜﻴﺮﺍ ﻓﻲ ﺻﻮﺭﺓ ﺍﻟﻨﻄﺎﻕ ﺍﻟﻤﻜﺎﻧﻲ ، ﺗﻈﻬﺮ ﺑﻀﻐﻂ ﺍﻗﻞ ﻓﻲ ﺻﻮﺭﺓ ﺍﻟﻨﻄﺎﻕ ﺍﻟﺘﺮﺩﺩﻱ ﻣﻤﺎ ﻳﻮﻓﺮ ﻣﻌﻠﻮﻣﺎﺕ ﺍﻛﺜﺮ ﻟﻠﺒﺤﺚ. ﻭﺇﺫﺍ ﻛﺎﻥ ﻫﻨﺎﻙ ﺧﻄﻮﻁ ﻗﻮﻳﺔ ﻟﺼﻮﺭﺓ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻓﻲ ﺍﻟﻤﺠﺎﻝ ﺍﻟﻤﻜﺎﻧﻲ ﻓﺎﻧﻬﺎ ﺳﺘﻌﻄﻲ ﺍﻟﻜﺜﻴﺮ ﻣﻦ ﺍﻟﻤﻌﻠﻮﻣﺎﺕ ﻓﻲ ﺍﻟﻤﺠﺎﻝ ﺍﻟﺘﺮﺩﺩﻱ ﺧﺎﺻﺔ ﻟﻠﺨﻄﻮﻁ ﺑﺎﻻﺗﺠﺎﻩ ﺍﻟﻌﺎﻣﻮﺩﻱ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﺳﺘﺨﻼﺹ ﺍﻟﺨﺼﺎﺋﺺ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺗﻤﺜﻴﻞ ﺧﺼﺎﺋﺺ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻳﺘﻢ ﻭﺻﻒ ﺍﻟﻤﻴﺰﺍﺕ ﺑﺎﺳﺘﺨﺪﺍﻡ ﻧﻈﺎﻡ ﺍﻻﺣﺪﺍﺛﻴﺎﺕ ﺍﻟﻘﻄﺒﻴﺒﻴﺔ ) polar ، (r, θ) (coordination ﻟﺘﻤﺜﻴﻞ ﺍﻟﺼﻮﺭ ﻓﻲ ﺍﻟﻨﻄﺎﻕ ﺍﻟﺘﺮﺩﺩ. ﺷﺪﺓ ﺍﻟﺘﻐﻴﺮ ﻓﻲ ﺍﻟﻤﻴﻞ ﺧﻼﻝ ﻗﻴﻢ) (r ﻳﻌﺒﺮ ﻋﻦ ﺷﺪﺓ ﺍﻟﺘﺠﺎﻋﻴﺪ ﻋﻠﻰ ﺑﺼﻤﺔ ﺍﻟﻜﻒ) . (palmprint’s creases ﻭ ﻗﻴﻢ ﺍﻟﺰﺍﻭﻳﺔ ) ( θ ﻳﺪﻝ ﻋﻠﻰ ﺍﺗﺠﺎﻫﺎﺕ ﺍﻟﻄﻮﻳﺎﺕ) (creases ﻓﻲ ﺑﺼﻤﺔ ﺍﻟﻜﻒ .

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺗﻤﺜﻴﻞ ﺧﺼﺎﺋﺺ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺻﻮﺭﺓ ﻳﻤﻜﻦ ﺗﺤﻮﻳﻠﻬﺎ ﻣﻦ ﻧﻈﺎﻡ ﺍﺣﺪﺍﺛﻴﺎﺕ ﺍﻟﺰﺍﻭﻳﺔ ﺍﻟﻴﻤﻨﻰ ) ( right-angle coordination system ﺍﻟﻰ ﻧﻈﺎﻡ ﺍﻻﺣﺪﺍﺛﻴﺎﺕ ﺍﻟﻘﻄﺒﻴﺔ ) (polar coordination system ﻣﻦ ﺧﻼﻝ ﺍﺳﺘﺨﺪﺍﻡ ﺍﻟﻤﻌﺎﺩﻟﺔ ﺍﻟﺘﺎﻟﻴﺔ :

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺗﻤﺜﻴﻞ ﺧﺼﺎﺋﺺ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻟﺘﻤﺜﻴﻞ ﺷﺪﺓ ﺍﻟﻄﻮﻳﺎﺕ ) (crease ﻋﻠﻰ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ، ﻳﺘﻢ ﺗﻘﺴﻴﻢ ﺻﻮﺭﺓ ﻧﻄﺎﻕ ﺍﻟﺘﺮﺩﺩ ﺍﻟﻰ ﺳﻠﺴﻠﺔ ﻣﻦ ﺍﻟﺪﻭﺍﺋﺮ ﺍﻟﺘﻲ ﻟﻬﺎ ﻧﻔﺲ ﺍﻟﻤﺮﻛﺰ، ﻛﻤﺎ ﻫﻮ ﻣﺒﻴﻦ ﻓﻲ ﺍﻟﺸﻜﻞ.

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺗﻤﺜﻴﻞ ﺧﺼﺎﺋﺺ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﻟﺘﻤﺜﻴﻞ ﺍﻻﺗﺠﺎﻩ ﻋﻠﻰ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ، ﻳﺘﻢ ﺗﻘﺴﻴﻢ ﺻﻮﺭﺓ ﻧﻄﺎﻕ ﺗﺮﺩﺩ ﻣﻦ ﺧﻼﻝ ﺳﻠﺴﻠﺔ ﻣﻦ ﺍﻟﺨﻄﻮﻁ ﺍﻟﺘﻲ ﺗﻤﺮ ﻋﺒﺮ ﻭﺳﻂ ﺍﻟﺼﻮﺭﺓ ، ﻛﻤﺎ ﻫﻮ ﻣﺒﻴﻦ ﻓﻲ ﺍﻟﺸﻜﻞ ). ( b

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺗﻤﺜﻴﻞ ﺧﺼﺎﺋﺺ ﺑﺼﻤﺔ ﻛﻒ ﻻﺳﺘﺨﺮﺍﺝ ﻗﻴﻢ ﺍﻟﺨﺼﺎﺋﺺ ﻝ ) ، (r ﻧﺴﺘﺨﺪﻡ ﺍﻟﻤﻌﺎﺩﻟﺔ ﺍﻟﺘﺎﻟﻴﺔ : ﻻﺳﺘﺨﺮﺍﺝ ﻗﻴﻢ ﺍﻟﺨﺼﺎﺋﺺ ﻝ) ، (θ ﻧﺴﺘﺨﺪﻡ ﺍﻟﻤﻌﺎﺩﻟﺔ ﺍﻟﺘﺎﻟﻴﺔ :

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﻣﻄﺎﺑﻘﺔ ﻭﺗﺼﻨﻴﻒ ﺍﻟﺨﺼﺎﺋﺺ ﻣﻄﺎﺑﻘﺔ ﺍﻟﺨﺼﺎﺋﺺ ﺗﺘﻢ ﻟﻘﻴﻢ r ﻭ θ , ﻭﺗﺘﻢ ﻋﻤﻠﻴﺔ ﺍﻟﻤﻄﺎﺑﻘﺔ ﻣﻦ ﺧﻼﻝ ﺣﺴﺎﺏ ﺍﻟﻤﺴﺎﻓﺔ ﻟﺨﺼﺎﺋﺺ ﻗﻴﻢ r ﻭ ﺣﺴﺎﺏ ﺍﻟﻤﺴﺎﻓﺔ ﻟﺨﺼﺎﺋﺺ ﻗﻴﻢ θ ﻛﺎﻟﺘﺎﻟﻲ:

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﻣﻄﺎﺑﻘﺔ ﺍﻟﺨﺼﺎﺋﺺ ﻭﺍﻟﺘﻌﺮﻑ ﻣﻦ ﺧﻼﻝ ﺑﺼﻤﺔ ﺍﻟﻜﻒ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﻣﻄﺎﺑﻘﺔ ﺍﻟﺨﺼﺎﺋﺺ ﻭﺍﻟﺘﻌﺮﻑ ﻣﻦ ﺧﻼﻝ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺍﻟﻤﺠﻤﻮﻋﺔ ،A ﻭﺍﻟﺬﻱ ﻳﺘﻀﻤﻦ ﻋﻴﻨﺎﺕ ﻟﻨﻔﺲ ﺍﻟﻜﻒ ﺍﻱ ﻟﻨﻔﺲ ﺍﻟﺸﺨﺺ ، ﻭ ﻣﻌﺪﻝ ﺍﻟﻤﺴﺎﻓﺔ ﻟﺨﺼﺎﺋﺺ r ﻫﻲ 7. 3 ﻭ ﻭﺍﻟﺨﺼﺎﺋﺺ θ ﻫﻲ ﻗﺮﻳﺒﺔ ﺟﺪﺍ ﻣﻦ ﺑﻌﻀﻬﺎ ﺍﻟﺒﻌﺾ. ﺗﺸﻤﻞ ﺍﻟﻤﺠﻤﻮﻋﺔ ﺍﻟﺜﺎﻧﻴﺔ ﻋﻴﻨﺎﺕ ﻟﺒﺼﻤﺎﺕ ﻛﻒ ﻻﺷﺨﺎﺹ ﻣﺨﺘﻠﻔﻴﻦ ﻟﻜﻦ ﺍﻟﻌﻴﻨﺎﺕ ﻣﺘﻤﺎﺛﻠﺔ ، ﺣﻴﺚ ﻣﻌﺪﻝ ﺍﻟﻤﺴﺎﻓﺔ ﻟﺨﺼﺎﺋﺺ r ﻫﻲ 4. 9 ﻭﺧﺼﺎﺋﺺ θ ﻫﻲ ﻗﺮﻳﺒﺔ ﻣﻦ ﺑﻌﻀﻬﺎ ﺍﻟﺒﻌﺾ. ﺍﻟﻤﺠﻤﻮﻋﺔ ،C ﺍﻟﺬﻱ ﺗﻌﻄﻲ ﻋﻴﻨﺎﺕ ﻟﺒﺼﻤﺎﺕ ﻛﻒ ﻻﺷﺨﺎﺹ ﻣﺨﺘﻠﻔﻴﻦ، ﻣﻌﺪﻝ ﺍﻟﻤﺴﺎﻓﺔ ﻟﺨﺼﺎﺋﺺ r ﻫﻲ 0. 02 ﻭ ﻭﺍﻟﺨﺼﺎﺋﺺ θ ﻻ ﺗﺰﺍﻝ ﺑﻌﻴﺪﺓ ﻋﻦ ﺑﻌﻀﻬﺎ ﺍﻟﺒﻌﺾ

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻨﺘﺎﺋﺞ ﺍﻟﺘﺠﺮﻳﺒﻴﺔ ﻭﺗﻈﻬﺮ ﺍﻟﻨﺘﺎﺋﺞ ﺍﻟﺘﺠﺮﻳﺒﻴﺔ ﺃﻦ ﺑﺼﻤﺔ ﺍﻟﻜﻒ ﺗﺴﺎﻋﺪ ﻋﻠﻰ ﺗﺤﺪﻳﺪ ﺍﻟﻬﻮﻳﺔ ﺍﻟﻘﺎﺋﻤﺔ ﻋﻠﻰ ﺍﺳﺘﺨﺮﺍﺝ ﺍﻟﺨﺼﺎﺋﺺ ﻓﻲ ﻣﺠﺎﻝ ﺍﻟﺘﺮﺩﺩ ﻭ ﺍﻟﺠﺪﻭﻝ ﺍﻟﺘﺎﻟﻲ ﻳﺒﻴﻦ ﺫﻟﻚ.

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﺼﺎﺩﺭ [1] Zhang, D. (2004). Palmprint Authentication, Kluwer Academic Publishers. [2] Identification of a Person by Palm Geometry Using Invariant Features. [3] Zhang, D. & Liu, L. L. (2009). Palmprint Features, In Encyclopedia of Biometrics, S. Z. Li, pp. 1043 -1049, Springer. [4] Wu, X. , Wang, K. & Zhang, D. (2004 a). A novel approach of palm-line extraction. Proceeding of the Third International Conference on Image and Graphics, pp. 230– 233 [5] Boles, W. & Chu, S. (1997). Personal identification using images of the human palms. Proceedings of IEEE Region 10 Annual Conference, Speech and Image Technologies for Computing and Telecommunications, Vol. 1, pp. 295– 298 [6] Rafael. Diaz, M. , Travieso, C. , Alonso, J. & Ferrer, M. (2004). Biometric system based in the feature of hand palm. Proceedings of 38 th Annual International Carnahan Conference on Security Technology, pp. 136– 139

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﺼﺎﺩﺭ [7] Kung, S. , Lin, S. & Fang, M. (1995). A neural network approach to face/palm recognition. Proceedings of IEEE Workshop on Neural Networks for Signal Processing, pp. 323– 332. [8] Huang, D. , Jia, W. & Zhang, D. (2008). Palmprint verification based on principal lines. Pattern Recognition, Vol. 41, No. 4, pp. 1316– 1328 [9] Wu, X. , Wang, K. , & Zhang, D. (2004 b). Palmprint recognition using directional energy feature. Proceedings of International Conference on Pattern Recognition, Vol. 4, pp. 475– 478 [10] Wu, X. , Wang, K. , & Zhang, D. (2004 b). Palmprint recognition using directional energy feature. Proceedings of International Conference on Pattern Recognition, Vol. 4, pp. 475– 478 [11] Lu, G. , Zhang, D. & Wang, K. (2003). Palmprint recognition using eigen palms features. Pattern Recognition Letters, Vol. 24, No. 9, pp. 1463– 1467 [12] Kumar, K. V. & Negi, A. (2007). A novel approach to eigenpalm features using feature partition ing framework. Conference on Machine Vision Applications, pp. 29 -32. [13] Connie, T. , Jin, A. , Ong, M. & Ling, D. (2005). An automated palmprint recognition system. Image and Vision Computing, Vol. 23, No. 5, pp. 501– 515

ﺍﻟﻘﻴﺎﺳﺎﺕ ﺍﻟﺤﻴﻮﻳﺔ ﺍﻟﻤﺼﺎﺩﺭ [14]Yang, J. , Zhang, D. , Yang, J. & Niu, B. (2007). Globally maximizing locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics. IEEE Transactions on Pattern Analysisand Machine Intelligence, Vol. 29, No. 4, pp. 650– 664. [17] Duta, N. , Jain, A. & Mardia, K. (2002). Matching of palmprint. Pattern Recognition Letters, Vol. 23, pp. 477 -485 [18] Huang, D. , Jia, W. & Zhang, D. (2008). Palmprint verification based on principal lines. Pattern Recognition, Vol. 41, No. 4, pp. 1316– 1328 -1316 ﺹ