Assessment of near infrared LED radiation pattern using Otsu thresholding

This paper describes the use of Otsu thresholding method in assessing the radiation pattern emitted by near infrared (NIR) LED. The NIR LED configured in this paper is intended to be used as illumination source for the development of a NIR palm vein image acquisition device. The experiment is conducted using a single board computer (SBC) to promote a real-time embedded system development that can be readily integrated as a vein viewing device. Based on the Otsu thresholded image obtained, it is observed that the NIR LED radiation pattern can be accessed subjectively through the thresholding process. The resulted thresholded image can be used as preliminary assessment of the radiation pattern in developing a NIR image acquisition system that fully utilizes the NIR LED properties

Development of arm-based application system

The aim of this paper is to expose the development process and software involved in realizing an ARM-based application system. The application system consists of a cruise algorithm inten dcd to be used in an autonomous robot prototype, which is developed with the help of Flowcode software that utilizes flowcharts as its design entry. The flowchart is then configured to be tested for real-world application over £-blocks board integrated with an ARM-based microcontroller chip from Atmel, AT91SAM7S128. It is hoped that the development process shared in this paper may be benefitted for researchers who wishes to start developing an ARM-based system for further study or other purpose in one way or another

Smart Data Recognition System For Seven Segment LED Display

The automatic data capturing system provides an alternative and effective way of data collection instead of manual data collection in the laboratory, especially for experiments that need to be carried out for a long period. It can solve common mistakes made by humans, like misreading or mistyping data. Thus, a new smart data recognition system for a seven-segment LED display is developed to sort the whole process of data collection to become more systematic and accurate. An image is captured and saved automatically in an image file, and then it is processed through MATLAB software to identify the digits displayed on the LED display. Once the image is preprocessed, analyzed, and recognized, the final output values obtained are transferred to an existing Excel file for a further process according to the user’s requirement. From the results obtained, it was proven that binary thresholding is the best preprocessing method, and the brightness of the image should be set to ‘0’ for better recognition output

Acquiring palm vein patterns for visual interpretation

This paper presents the setup required in acquiring palm vein pattern for visual interpretation based on the use of near infrared (NIR) spectrum illumination. It uses the NIR peak wavelengths of 0.830 μm, 0.850 μm, 0.870 μm, 0.880 μm and 0.890 μm in the experiment with variation to the number of layers used as the filtering material. The effect of the NIR peak wavelengths are observed and discussed, in terms of the visibility of the vein pattern in the image acquired. Other issues that influence the visibility of the vein pattern in the developed setup are also presented. The developed experimental setup can be extended its usage in acquiring vein pattern in other parts of human body, which can be used for biometric, medical or device prototyping purpose

Overview and challenges of palm vein biometric system

Palm vein biometric system is one of the biometric technologies that has grabbed the attention of scholarly researchers and industrial alike, due to its distinctive properties and hidden nature. Constant effort had been done in improving the palm vein biometric system performance through the design of its vein acquisition system and vein image analysis. This paper provides an overview of the underlying elements of a palm vein biometric system that summarises the works done, and predicts the upcoming research focus in this area

Review on a palm vein infrared image acquisition system

Palm vein recognition system is one of the modalities in biometric technology. It manipulates the vast pattern of vein under the palm as one of the measurements in validating a person with his or her claimed identity. One of the components in a palm vein recognition system is the image acquisition part. With a suitable configuration, the image acquisition system may capture the required image, just enough for the recognition process. This paper is a brief review on the development needs of a palm vein image acquisition system

Demonstration Of Palm Vein Pattern Biometric Recognition By Machine Learning

This paper aims to demonstrate the extraction of palm vein pattern features by local binary pattern (LBP) and its different recognition rate by two types of classification methods. The first classification method is by K-nearest neighbour (KNN) while the second method is by a support vector machine (SVM). Whilst SVM is optimized for direct classifications between two classes, the KNN is best for multi-class classifications. Based on the biometric recognition framework shared in this paper, both techniques shared comparable performance in terms of the recognition rate. The differences in the recognition rate can only be seen if the LBP features extracted for the classification are different. In general, a higher recognition rate can be achieved for palm vein pattern biometric system if all LBP bins are used for the classification, compared to if only selected features are used for the purpose. The best recognition rate that can be achieved by the three datasets demonstrated in this paper are 60%, 70% and 100% respectively for the CASIA, PolyU and self-dataset. It shows that different input dataset may behave differently even by using the same machine learning approach in its biometric recognition process

Image Histogram: Preliminary Findings of Anti-Spoofing Mechanism for Hand Biometrics Acquisition

Biometrics data are prone to spoofing activities especially on its sensor levels where fake biometrics data can be generated to imitate genuine biometrics data. Fake biometrics are false biometrics data that resemble genuine biometrics characteristics. If fake biometrics is accepted by a biometrics system, the possibility of personal information and data to be stolen is high. The consequences lie in the unwanted access, and the public may become insecure to use biometrics as an authentication tool. Biometrics acquisition process with an added detection mechanism can help distinguish between genuine and fake biometrics data. It is possible by the use of near-infrared (NIR) light during acquisition process because the interaction between NIR light with human skin and fake biometrics are different; due to the living trait property possessed by a human. This paper shares preliminary findings of image histogram for both genuine and fake biometrics images acquired by NIR illumination. Observation on the image histogram reveals that there are differences to the image properties that can be used to distinguish the genuine and fake biometrics data. The approach can be extended to its usage as a detection mechanism for other biometrics data as well. The main principle lies in the difference of image response between genuine and fake biometrics data acquired by the NIR illumination

Classification of eye abnormality using statistical parameters in texture features of corneal arcus image

The corneal arcus (CA), is the white-gray sediments, exist within the iris-limbus like a circle ring, caused by the occurrence of lipid disorder, in the bloodstream. This sign shows, the indication to diseases such as the coronary heart disease, diabetes, and hypertension. This paper demonstrates the classification of the CA as an indicator of hyperlipidemia. The experiment, uses two sets of sample data, consisting of the normal and abnormal eyes (i.e., CA), for classifies each group. The step for this classification, begin with the normalization of the eye images (as part of pre-processing), to achieve the region of interest (ROI). The next process is to extract the image texture using the grey level co-occurrence matrix (GLCM) technique, and calculate the extraction of the image texture using the statistical method. These features, then will be fed into the classifier, as the input for several processes, namely as the data training, data testing and validation data. In these experiments, we have obtained the excellent result using the proposed framework. This proves that, by using a Bayesian regularization (BR) classifier, the results of this classification given by the sensitivity (94%), specificity (100%), and accuracy (97.78%). Applications/Improvements: Based on the results obtained, the proposed system is successfully to classify the images with the CA signs. This show that, this proposed method can be applied to identify the presence of the hypercholesterolemia in a non-invasive test, to classify and detect the image of the CA