Study of Hand-Dorsa Vein Recognition

Wang, Yiding; Li, Kefeng; Cui, Jiali; Shark, Lik‐Kwan; Varley, M.R.

doi:10.1007/978-3-642-14922-1_61

Cited by 33 publications

(20 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The LBP operator has been proposed for face recognition [2,3], finger vein recognition [8], dorsal hand vein recognition [14,15] and palm-print recognition [10]. The LDP operator has been proposed for face recognition [17] and finger vein recognition [7,6].…”

Section: Introductionmentioning

confidence: 99%

Palm vein recognition with Local Binary Patterns and Local Derivative Patterns

Mirmohamadsadeghi

Drygajlo

2011

2011 International Joint Conference on Biometrics (IJCB)

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Palm vein recognition with Local Binary Patterns and Local Derivative Patterns

Mirmohamadsadeghi

Drygajlo

2011

2011 International Joint Conference on Biometrics (IJCB)

View full text Add to dashboard Cite

“…An even more comprehensive feature vector can be provided by partition of each image in the spatial and wavelet domains into non-overlapping regions, and concatenation of the multi-radius LBP histograms from each region to provide position-based local grey-level appearance (Wang et al, 2010;Wang et al, 2011). In the implementation, the same image size is maintained for each partitioned block at different image scales.…”

Section: Multi-radius Local Binary Patternsmentioning

confidence: 99%

Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

Wang

Duan

Shark

et al. 2016

IJCAT

Self Cite

View full text Add to dashboard Cite

Among biometric modalities, hand vein patterns are seen as providing an attractive method for high-level security access applications owing to high impenetrability and good user convenience. For biometric recognition based on near-infrared dorsal hand vein images, Local Binary Patterns (LBP) have emerged as a highly effective descriptor of local image texture with high recognition performance reported. In this paper, the traditional approach with LBP applied in the spatial domain is extended to multi-radius LBP in the wavelet domain to provide a more comprehensive set of feature categories to capture grey-level variation characteristics of vein patterns, and score weighted fusion based on the relative discriminative power of each feature category is proposed to achieve higher recognition performance. The proposed methodology is shown to provide a more robust performance with a recognition rate in excess of 99% and an equal error rate significantly less than 2%.

show abstract

“…Minimum distance classifier is used for matching. Wanget al [7] presented another approach of hand vein recognition system .Their approach was based on partitioning local binary pattern and further was assessed using a similarity measure which was obtained by calculating chi square statistic between the feature vectors of tested sample and the target sample. Wang et al [8] proposed another approach for recognition which was based on partitioning of local binary pattern (LBP).In their work after preprocessing the image is divided into subimages.LBP features are extracted from all sub images.…”

Section: Literature Surveymentioning

confidence: 99%

Hand Vein Authentication System based on Information Sets

Parashar¹,

Biswas²,

Grover³

2016

IJCA

View full text Add to dashboard Cite

Biometrics have been used from ages for authentication purpose but the key advantages of hand vein pattern that is "hand vein pattern are 100 times more unique than other physiological features" are attracting many researchers. In this paper we have proposed an efficient authentication system using hand vein pattern. The concept of information sets has been used for feature extraction so as to have control over the local information. The information set based features has already been implemented on biometrics like ear and face. Till now these features have not been implemented on hand vein pattern based verification systems hence we have explored their suitability on hand veins. All the experiments have been carried out on hand vein database developed at IIT, DELHI .We have used Hanman classifier to check the accuracy of extracted features. Then we have optimized the Hanman classifier performance by using genetic algorithm. The optimized results shows that the proposed system in 99% efficient. We have also compared Hanman classifier accuracy with accuracy obtained from knearest neighbor and SVM classifier.

show abstract

Study of Hand-Dorsa Vein Recognition

Cited by 33 publications

References 7 publications

Palm vein recognition with Local Binary Patterns and Local Derivative Patterns

Palm vein recognition with Local Binary Patterns and Local Derivative Patterns

Improving hand vein recognition by score weighted fusion of wavelet-domain multi-radius local binary patterns

Hand Vein Authentication System based on Information Sets

Contact Info

Product

Resources

About