Near-infrared Hand Vein Recognition Algorithm Research

Vein pattern recognition for human body is a newly coming touch-less biometrics, which holds great applicable vision on security and identification. The research of vein authentication technology started from around the year thousand and seemed to be very appealing to all the researchers although the developing system is not sophisticated.Compared to other kind of biometric authentication technology, it has lots of advantages.Human vein cannot be forged or stolen since its uniqueness and stability. And the hand vein authentication system is also easy and comfortable for people to use.That is the reason why people are putting much more energy and expectation into the research work to promote this technology.Comparing to the face recognition, fingerprint recognition and other biometrics, vein pattern recognition is also facing many problems in consumer system implementation.As there are still some problems such as environment lighting, algorithm complexity etc, vein recognition is really hard to be used in biometric security system.Based on the problems mentioned above, this article has made researches on the following aspects:1.Palm-dorsal vein Biometrics based on watershed algorithmThis approach is based on watershed algorithm to detect the single pixel level skeleton of the hand vein.Using the ending point and the crossing point of the extracted vein skeleton as the feature point, then measure the similarity between the registered vein image and the sampled vein image using Hausdorff distance. Experiments were conducted on our own palm-dorsal vein image database and the results are satisfactory. This approach avoids the noise problem and glitch problem happened in the thinning stage in traditional vein image recognition.2.Palm-dorsal vein recognition based on Gabor phase encoding methodFirstly, this part of the article discusses the shortage of the traditional vein image recognition based on the vein skeleton feature and then presents the vein image recognition based on Gabor phase encoding approach.The approach uses 2D Gabor filter phase encoding to represent the texture feature of the vein image and uses Hamming distance to evaluate the matching degree.Without considering of the geometric transformation, experimental results on our own palm-dorsal vein image database indicates the vein pattern biometric is potentially a useful biometric.3.Local Gabor phase feature for Palm-dorsal vein recognitionThis part of the article presents the local Gabor phase feature for vein recognition approach based on the Gabor encoding method discussed in last section. The approach combined the local Gabor feature with the global feature by using a local XOR pattern operator to extract the local variance feature of the Gabor phase encoding and using histogram method to represent the global feature.Chi-square distance is applied to verify the efficiency of our method, experiments were carried on our own palm-dorsal vein image database.The experimental results show that the local Gabor phase feature could provide sufficient information for vein recognition and robust to geometric transformation.