The invention provides a method and a device for recognizing and matching a palm vein. A palm vein image is acquired, image pretreatment is carried out on the palm vein image, and a to-be-recognized region of interest is acquired. Based on the region of interest, palm vein features are extracted, the extracted palm vein features are matched with pre-stored palm vein features in a database, and when matching is realized, preset operation is executed. Thus, distinct and accurate palm vein image information can be obtained, and interference with palm vein feature matching and bad influences on palm vein feature recognition are reduced.

Method and device for recognizing and matching palm vein

Palm vein recognition (PVR) refers to the contactless biometric identification method that uses palm vein patterns to confirm the identity of a person. Compared with other methods, PVR has received a wide attention because it provides more secure results. The veins, being located inside the human body, make PVR robust against tempering and changes in morphology of body features. Most PVR systems integrate four stages: image acquisition, pre-processing, feature extraction, and decision. The first two stages determine accuracy of the final identification results. Focusing on the pre-processing component, we discovered that the available approaches fail to generate more informative vein patterns by simultaneously suppressing noise and blur, and also by recovering semantically useful features (edges, contours, and lines) from the acquired images. This weakness calls for sophisticated acquisition devices that make PVR systems costly. In this work, we have proposed multiframe super-resolution (MSR) as a pre-processing stage to improve performance of the traditional PVR systems. MSR exploits information from multiple images of the same scene to reconstruct a high-resolution image. This technique signals the possibility of using inexpensive low-resolution imaging devices demanded by the traditional PVR systems. Experiments show that our method outperforms most classical methods.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-bmt.2019.0016

Cost‐effective and accurate palm vein recognition system based on multiframe super‐resolution algorithms

The invention discloses a vein pixel point judgment-based palm vein identification method. The method comprises the steps of obtaining a direction characteristic code matrix database of a palm vein image sample library; when a direction characteristic code matrix is obtained, extracting an ROI image from a palm vein image firstly; traversing an ROI of the palm vein image by adopting a sliding window; for an ROI sub-region obtained by each sliding, judging whether pixel points in the sub-region are vein pixel points or not by utilizing a direction template; if the pixel points in the sub-regionare the vein pixel points, obtaining a direction characteristic of the ROI sub-region by utilizing the direction template; if the pixel points in the sub-region are not the vein pixel points, settingthe direction characteristic of the sub-region to be -1, thereby obtaining a direction characteristic matrix; according to a preset direction characteristic code table, obtaining the direction characteristic code matrix; obtaining the direction characteristic code matrix of the to-be-identified palm vein image; and matching the direction characteristic code matrix of the to-be-identified palm vein image with each direction characteristic code matrix in the direction characteristic code matrix database to obtain an identification result. The method can improve the identification efficiency under the condition of ensuring the identification rate.

Vein pixel point judgment-based palm vein identification method

The invention provides a hand dorsum vein image identification processing method and a hand dorsum vein image identification processing device. Binarization processing of acquired hand dorsum vein images is carried out to acquire binary images including hand dorsum venous lines. The image integration processing of a plurality of rotated binary images is carried out along the vertical axes of the binary images, and a plurality of integral vectors are acquired. The normalization processing of the plurality of integral vectors is carried out to acquire the binary integral images of the binary images. The two-dimensional discrete cosine transform processing of the binary integral images is carried out to acquire low-frequency images, and to-be-identified characteristics are extracted from the low-frequency images. The binary images are rotated a plurality of times, and the image integration processing of the binary images is carried out after each time of rotation, and therefore the characteristic accuracy of the finally-acquired to-be-identified characteristics is higher, the characteristic stability thereof is stronger, and therefore the robustness of identification results of identity identification adopting the to-be-identified characteristics is higher.

Hand dorsum vein image identification processing method and hand dorsum vein image identification processing device

Disclosed herein are methods for identifying cells undergoing reprogramming and reprogrammed cells from a fluorescence microscopic image of one or more cells. According to some embodiments, the method includes an image processing step, a cell detection step, and, optionally, a clustering step.

Method and apparatus for detecting cell reprogramming

A system includes an infrared camera, an infrared light source, and a processor. The processor is programmed to receive, from the infrared camera, an image of a hand illuminated using the infrared light source, and send the image to a remote server to identify a user corresponding to the hand according to a vein pattern of the hand. An image of a hand illuminated using an infrared light source is received from an infrared camera. Region-of-interest segmentation is performed on the image to generate a segmented image of consistent hand location and orientation. Feature extraction is performed on the segmented image to generate a feature-extracted vein image. Matching of the feature-extracted vein image is performed against a database of feature-extracted vein images to identify a user identity corresponding to the hand.

Palm vein-based low-cost mobile identification system for a wide age range

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Palm vein-based low-cost mobile identification system for a wide age range