Pose Invariant Palm Vein Identification System using Convolutional Neural Network
Palm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm ima...
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University of Baghdad, College of Science for Women
2018-12-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/147 |
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| description | Palm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems. This paper proposed a pose invariant identification system for contactless palm vein which include three main steps, at first data augmentation is done by making multiple copies of the input image then perform out-of-plane rotation on them around all the X,Y and Z axes. Then a new fast extract Region of Interest (ROI) algorithm is proposed for cropping palm region. Finally, features are extracted and classified by specific structure of Convolutional Neural Network (CNN). The system is tested on two public multispectral palm vein databases (PolyU and CASIA); furthermore, synthetic datasets are derived from these mentioned databases, to simulate the hand out-of-plane rotation in random angels within range from -20° to +20° degrees. To study several situations of pose invariant, twelve experiments are performed on all datasets, highest accuracy achieved is 99.73% ∓ 0.27 on PolyU datasets and 98 % ∓ 1 on CASIA datasets, with very fast identification process, about 0.01 second for identifying an individual, which proves system efficiency in contactless palm vein problems. |
| format | Article |
| id | doaj-art-a25afec99b4d4b7595078f0bd66d0888 |
| institution | Kabale University |
| issn | 2078-8665 2411-7986 |
| language | English |
| publishDate | 2018-12-01 |
| publisher | University of Baghdad, College of Science for Women |
| record_format | Article |
| series | مجلة بغداد للعلوم |
| spelling | doaj-art-a25afec99b4d4b7595078f0bd66d08882025-08-20T03:37:03ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862018-12-0115410.21123/bsj.15.4.502-509Pose Invariant Palm Vein Identification System using Convolutional Neural NetworkBaghdad Science JournalPalm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems. This paper proposed a pose invariant identification system for contactless palm vein which include three main steps, at first data augmentation is done by making multiple copies of the input image then perform out-of-plane rotation on them around all the X,Y and Z axes. Then a new fast extract Region of Interest (ROI) algorithm is proposed for cropping palm region. Finally, features are extracted and classified by specific structure of Convolutional Neural Network (CNN). The system is tested on two public multispectral palm vein databases (PolyU and CASIA); furthermore, synthetic datasets are derived from these mentioned databases, to simulate the hand out-of-plane rotation in random angels within range from -20° to +20° degrees. To study several situations of pose invariant, twelve experiments are performed on all datasets, highest accuracy achieved is 99.73% ∓ 0.27 on PolyU datasets and 98 % ∓ 1 on CASIA datasets, with very fast identification process, about 0.01 second for identifying an individual, which proves system efficiency in contactless palm vein problems.http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/147biometrical identification, contactless Palm vein, convolutional neural network, region of interest |
| spellingShingle | Baghdad Science Journal Pose Invariant Palm Vein Identification System using Convolutional Neural Network مجلة بغداد للعلوم biometrical identification, contactless Palm vein, convolutional neural network, region of interest |
| title | Pose Invariant Palm Vein Identification System using Convolutional Neural Network |
| title_full | Pose Invariant Palm Vein Identification System using Convolutional Neural Network |
| title_fullStr | Pose Invariant Palm Vein Identification System using Convolutional Neural Network |
| title_full_unstemmed | Pose Invariant Palm Vein Identification System using Convolutional Neural Network |
| title_short | Pose Invariant Palm Vein Identification System using Convolutional Neural Network |
| title_sort | pose invariant palm vein identification system using convolutional neural network |
| topic | biometrical identification, contactless Palm vein, convolutional neural network, region of interest |
| url | http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/147 |
| work_keys_str_mv | AT baghdadsciencejournal poseinvariantpalmveinidentificationsystemusingconvolutionalneuralnetwork |