Finger Vein Recognition Using Local Mean Based K-Nearest Centroid Neighbor Classifier

2012 ◽  
Vol 628 ◽  
pp. 427-432 ◽  
Author(s):  
Ali Khalili Mobarakeh ◽  
Sayedmehran Mirsafaie Rizi ◽  
Saba Nazari ◽  
Jiang Ping Gou ◽  
Bakhtiar Affendi Rosdi
Keyword(s):  
Nearest Neighbor ◽  
Identification System ◽  
K Nearest Neighbor ◽  
Recognition Method ◽  
Finger Vein ◽  
New Type ◽  
Local Mean ◽  
Vein Recognition ◽  
Finger Vein Recognition ◽  
Neighbor Classifier

One of the newest methods of identification system is finger vein recognition which is a unique and successful way to identify human based on the physical characteristics of finger vein patterns. In this paper, a new type of classifier called Local Mean based K-nearest centroid neighbor (LMKNCN) is applied to classify finger vein patterns. Finally, the significance of the proposed method is proven by comparing the results of LMKNCN classifier with traditionally used K nearest neighbor classifier (KNN). The experimental results indicate that the proposed method in this research confidently merits the performance of the finger vein recognition method, as the gained accuracy using the proposed method is higher than that of the traditionally used method KNN. The maximum obtained accuracy of LMKNCN test with 2040 number of finger vein images is 100% while for KNN is 98.53%.

Near Infrared Finger Vein Recognition Method Based on Subspace Projection Technology

2014 ◽  
Vol 1030-1032 ◽  
pp. 2382-2385 ◽  
Author(s):  
Lin Lin Fan ◽  
Hui Ma ◽  
Ke Jun Wang ◽  
Yong Liang Shen ◽  
Ying Shi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Recognition System ◽  
Critical Issue ◽  
Subspace Projection ◽  
Threshold Method ◽  
Finger Vein ◽  
Vein Recognition ◽  
Finger Vein Recognition

Finger vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. Finger vein recognition faces a number of challenges. One critical issue is the performance of finger vein recognition system. To overcome this problem, a finger vein recognition algorithm based on one kind of subspace projection technology is presented. Firstly, we use Kapur entropy threshold method to achieve the purpose of intercepting region of finger under contactless mode. Then the finger vein features were extracted by 2DPCA method. Finally, we used of nearest neighbor distance classifier for matching. The results indicate that the algorithm has good recognition performance.

Finger Vein Recognition Based on Stable and Discriminative Superpixels

2016 ◽  
Vol 30 (06) ◽  
pp. 1650015 ◽  
Author(s):  
Lizhen Zhou ◽  
Gongping Yang ◽  
Yilong Yin ◽  
Lu Yang ◽  
Kuikui Wang
Keyword(s):  
Recognition Performance ◽  
Fusion Method ◽  
Recognition Method ◽  
Biometric Technology ◽  
Training Images ◽  
Finger Vein ◽  
Vein Recognition ◽  
Matching Score ◽  
Finger Vein Recognition ◽  
Better Than

Finger vein pattern, as a promising hand-based biometric technology, has been well studied in recent years. In this paper, a new superpixel-based finger vein recognition method is presented. In the proposed method, we develop two types of effective superpixels, i.e. stable superpixel and discriminative superpixel to represent finger vein image and these superpixels are expected to play different roles in matching stage. In detail, the stable and discriminative superpixels are firstly learned from the training images for each enrolled class. When verifying a testing image, we just compare the superpixels at the same location as the two types of superpixels in template. Then, the two types of superpixels are combined utilizing a reversible weight-based fusion method in score level. Additionally, to further improve the recognition performance, we explore the superpixel context feature (SPCF). For each superpixel the SPCF is obtained by comparing the current superpixel with its surrounding neighbors. In the final matching stage, we integrate the matching score of two types of superpixels and it of the SPCF using the weighted SUM fusion method. The experimental results on two open finger vein databases, i.e. PolyU and SDUMLA-FV, show that our method not only performs better than the existing superpixel-based method, but also has advantages in comparison with some traditional ones.

A Local Mean Representation-based K -Nearest Neighbor Classifier

2019 ◽  
Vol 10 (3) ◽  
pp. 1-25 ◽  
Author(s):  
Jianping Gou ◽  
Wenmo Qiu ◽  
Zhang Yi ◽  
Yong Xu ◽  
Qirong Mao ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Classifier ◽  
Local Mean ◽  
Neighbor Classifier

scholarly journals HIGH ACCURACY RECOGNITION BIO METRICS BASED ON FINGER VEIN SCREENING SENSOR

2020 ◽  
Vol 3 (2) ◽  
pp. 35-46
Author(s):  
Shereen S. Jumaa ◽  
Khamis A. Zidan
Keyword(s):  
Nearest Neighbor ◽  
Low Cost ◽  
Computation Time ◽  
Recognition System ◽  
K Nearest Neighbor ◽  
Finger Print ◽  
Finger Vein ◽  
Vein Recognition ◽  
Histogram Of Gradients ◽  
Micro Lens

One of the safest biometrics of today is finger vein- but this technic  arises with some specific challenges, the most common  one being that the vein pattern is hard to extract because finger vein images are always low in quality, significantly  hampered the feature extraction and classification stages. Professional  algorithms want to be considered with the conventional hardware for capturing finger-vein images is  by using red Surface Mounted Diode (SMD) leds for this aim. For capturing images, Canon 750D camera with micro lens is used. For high quality images the integrated micro lens  is used, and with some adjustments it can also obtain finger print. Features extraction was used by a combination of Hierarchical Centroid and Histogram of Gradients. Results were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results displayed improvement as compared to three latest benchmarks in this field that used 6-fold validation and SDUMLA-HMT. The work novelty is owing to the hardware design of the sensor within the finger-vein recognition system to obtain, simultaneously, highly secured recognition with low computation time ,finger vein and finger print at low cost, unlimited users for one device and open source.

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