Contrast limited adaptive histogram equalization
Graphics gems IV
Machine Vision and Applications
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Minutiae feature analysis for infrared hand vein pattern biometrics
Pattern Recognition
Advances in Biometrics: Sensors, Algorithms and Systems
Advances in Biometrics: Sensors, Algorithms and Systems
Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profiles
IEICE - Transactions on Information and Systems
Personal authentication using hand vein triangulation and knuckle shape
IEEE Transactions on Image Processing
Palm vein extraction and matching for personal authentication
VISUAL'07 Proceedings of the 9th international conference on Advances in visual information systems
Vein segmentation in infrared images using compound enhancing and crisp clustering
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper, a vein pattern extraction method is proposed for biometric purposes. First, we utilize a maximal intra-neighbor difference (MIND) vector of all pixels in the original image to represent the relationship between each pixel and its neighborhood. Based on the MIND vectorgram (MINDVG), we define a maximal intra-neighbor vector difference (MIVND) as an index to unveil the preliminary vein pattern. Finally, we use an adaptive threshold to extract the venation pattern. The advantage of this method is that, by combining the features of vein imaging and the spatial properties of the MINDVG, the algorithm can efficiently overcome the negative factors of inhomogeneous thickness and blurry boundaries in vein imaging without preprocessing. Experiments on several images show that this method can directly extract intact and clear vein patterns with minimal noise. Therefore, the proposed algorithm has been validated in vein pattern extraction.