Biometric identification is an important guarantee for social security. In recent years, as the development of social and
economic, the more accuracy and safety of identification are required. The person identity verification systems that use a
single biometric appear inherent limitations in accuracy, user acceptance, universality. Limitations of unimodal biometric
systems can be overcome by using multimodal biometric systems, which combines the conclusions made by a number of
unrelated biometrics indicators. Aiming at the limitations of unimodal biometric identification, a recognition algorithm
for multimodal biometric fusion based on hand vein, iris and fingerprint was proposed. To verify person identity, the
hand vein images, iris images and fingerprint images were preprocessed firstly. The region of interest (ROI) of hand vein
image was obtained and filtered to reduce image noises. The multiresolution analysis theory was utilized to extract the
texture information of hand vein. The iris image was preprocessed through iris localization, eyelid detection, image
normalization and image enhancement, and then the feature code of iris was extracted from the detail images obtained
using wavelet transform. The texture feature information represented fingerprint pattern was extracted after filtering and
image enhancement. The Bayesian theorem was employed to realize the fusion at the matching score level and the fusion
recognition result was finally obtained. The experimental results were presented, which showed that the recognition
performance of the proposed fusion method was obviously higher than that of single biometric recognition algorithm. It
had verified the efficiency of the proposed method for biometrics.
As one kind of the latest forms of biometrics, the human hand vein recognition utilizes a state-of-the-art recognition
algorithm based on unique veins and capillaries found on human dorsal hand, which possesses the advantages such as
well anti-falsification and high noise immunity. For the hand vein recognition, the most important premise is acquiring
the high quality hand vein image. According to the special effect of human hand vein on the near infrared (NIR), when a
hand is scanned by an image sensor, the vein pattern appears darker than its surroundings. Depending on this
characteristic, the NIR light source was utilized to illuminate the image acquisition system for hand vein. And the
optimal parameters of light source were chosen and the light source with high uniformity illuminance was manufactured
to acquire the more clear hand vein image. Simultaneously, for the purpose of system miniaturization and design
flexibility, the embedded image acquisition system for hand vein was designed based on the technology of system on
programmable chip (SOPC). FPGA and CMOS image sensor were taken as the core components in the system, and the
hardware of acquisition module is realized by configuring NiosII soft-core CPU and some corresponding interface
modules on a FPGA. The software was developed by using the NiosII IDE to realize the initialization control to
CMOS image sensor and collection, storage and transmission for the image data gathered from CMOS. Then the
collected hand vein image was simply preprocessed, which further improved the image quality. Through experiments,
the results indicated that this system could obtain the hand vein image with high performance, and it supplied the
embedded development platform for hand vein recognition simultaneously. It was significant to develop the hand vein
recognition system with small size and high speed.
The flatness of pins is an important quality indicator for integrated circuit packaging. Almost all of the detection methods
which are currently used can't be successful on efficiency and precision. In this system, the image of IC pins was
captured by an properly optical systems and corresponding CCD sensor. To detect the edge of each pin, traditional
algorithmic, such as Sobel operator and Roberts operator, have some disadvantages: the edge is too thick for system to
accurately measure and the edge show directional character. An image segmentation and border extracting algorithm
focus on the extreme of neighborhood image intensity change was adopted. The advantage of this algorithm was each
pixel's neighborhood image intensity information was considered, so the algorithm is more suitable for accurately
measure. After edge was extracted, how to identify the useful spots is cast as a binary classification task. The support
vector machine (SVM) would be used to identify pin's spots. After proper image characteristics are obtained and a
certain amount of training, SVM provides higher discrimination ratio to distinguish spots of the IC pins. To measure the
flatness of pin, a particular line which can be identified easily should be put in the image as a baseline. Through
calculating the distance between the pins spot and baseline, the flatness of pins is obtained accurately. In this system, the
flatness of IC pins can be accurately and quickly measured, which is worthy of broad application prospect in IC
packaging.
Based on the unique characteristic, the paper currency numbers can be put into record and the automatic identification
equipment for paper currency numbers is supplied to currency circulation market in order to provide convenience for
financial sectors to trace the fiduciary circulation socially and provide effective supervision on paper currency.
Simultaneously it is favorable for identifying forged notes, blacklisting the forged notes numbers and solving the major
social problems, such as armor cash carrier robbery, money laundering. For the purpose of recognizing the paper
currency numbers, a recognition algorithm based on neural network is presented in the paper. Number lines in original
paper currency images can be draw out through image processing, such as image de-noising, skew correction,
segmentation, and image normalization. According to the different characteristics between digits and letters in serial
number, two kinds of classifiers are designed. With the characteristics of associative memory, optimization-compute and
rapid convergence, the Discrete Hopfield Neural Network (DHNN) is utilized to recognize the letters; with the
characteristics of simple structure, quick learning and global optimum, the Radial-Basis Function Neural Network
(RBFNN) is adopted to identify the digits. Then the final recognition results are obtained by combining the two kinds of
recognition results in regular sequence. Through the simulation tests, it is confirmed by simulation results that the
recognition algorithm of combination of two kinds of recognition methods has such advantages as high recognition rate
and faster recognition simultaneously, which is worthy of broad application prospect.
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