Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

Vein palm recognition model using fusion of features

One of the most promising mechanisms in the field of security and information safety is authentication based on palm vein. The main reasons that vein palm becomes an authentication method is because of its distinctive privacy, as it is difficult to manipulate or change its results, because of the location of the vein within the palm. With the use of this technology, it has become easy to maintain data from unauthorized access and unwanted persons. In this work proposed model are suggested that contain four stages to reach the results: in the first stage is the pre-processing stage where histogram equation was used to enhance the image and the properties are shown, the second stage is the extracting the properties where, Gabor filter and 2-discrete wavelet filters are suggested for features extraction, where it is considered one of the most important filters used to extract the features, as well as in the third stage "PCA" are used for data or features reduction, because of its advantages in analyzing the features and reducing the spacing between them. As for the last stage, the Euclidean distance used to measure the spacing. The results were acceptable and convincing, since the similarity ratio 96.2%. These results were obtained after several tests and using the Gabor filter with 2D-discrete wavelet transform and principal component analysis (PCA), I got the best results

Green strength optimization of injection molding proces for novel recycle binder system using Taguchi method

Metal injection molding is a worldwide technology that world use as a predominant method in manufacturing. Optimizing the injection molding process is critical in obtaining a good shape retention of green components and improving manufacturing processes itself. This research focuses on the injection molding optimization which correlated to a single response of green strength which implementing orthogonal array of Taguchi L9 (34). It involved the effect of four molding factors: injection temperature, mold temperature, injection pressure and injection speed, towards green strength. The significant levels and contribution to the variables of green strength are determined using the analysis of variance. Manual screening test is conducted in regards of identifying the appropriate level of each factors. The study demonstrated that injection temperature was the most influential factor contributes to the best green strength, followed by mold temperature, injection speed and injection pressure. The optimum condition for attaining optimal green strength was definitely by conducting injection molding at; 160 ºC of injection temperature, 40 ºC of mold temperature, 50 % of injection pressure and 50 % of injection speed. The confirmation experiment result is 15.5127 dB and it was exceeding minimum requirement of the optimum performance. This research reveals that the proposed approach can excellently solve the problem with minimal number of trials, without sacrificing the ability of evaluating the appropriate condition to achieve related response, which is green strength

A Survey on Methods of Image Processing and Recognition for Personal Identification

The network of blood vessels possesses several properties that make a good biometric feature for personal identification: (1) they are difficult to damage and modify; (2) they are difficult to simulate using a fake template; and (3) vein information can represent the liveness of the person. In the process of recognition of the network of blood vessels, we encounter two main difficulties: the first difficulty concerns the enhancement of the image of blood vessels obtained from the camera working in visible and/or infrared light, and the second one concerns the process of extraction of features and methods of classification. In the first part, this chapter presents the basic methods of preprocessing biometric images. In the second part, we discuss the process of feature extraction with particular emphasis on the feature extraction from images depicting the network of blood vessels. This applies to texture analysis using the co-occurrence matrix, Gabor filtration, moments, and topological features using cross points. In the third part, we present the methods of processing images of the blood vessel network of dorsal part of the hand and wrist. We also discuss the process of reducing the dimensionality of a feature vector using the principal components analysis method

Combination a Skeleton Filter and Reduction Dimension of Kernel PCA Based on Palmprint Recognition

Palmprint identification is part of biometric recognition, which attracted many researchers, especially when fusion with face identification that will be applied in the airport to hasten knowing individual identity. To accelerate the process of verification feature palms, dimension reduction method is the dominant technique to extract the feature information of palms.The mechanism will boost if the ROI images are processed prior to get normalize image enhancement.In this paper with three sample input database, a kernel PCA method used as a dimension reduction compared with three others and a skeleton filter used as a image enhancement method compared with six others. The final results show that the proposed method successfully achieve the target in terms of the processing time of $0.7415$ second, the EER performance rate of 0.19 % and the success of verification process about 99,82 %

Handbook of Vascular Biometrics

This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers

Biometric Person Identification Using Near-infrared Hand-dorsa Vein Images

Biometric recognition is becoming more and more important with the increasing demand for security, and more usable with the improvement of computer vision as well as pattern recognition technologies. Hand vein patterns have been recognised as a good biometric measure for personal identification due to many excellent characteristics, such as uniqueness and stability, as well as difficulty to copy or forge. This thesis covers all the research and development aspects of a biometric person identification system based on near-infrared hand-dorsa vein images. Firstly, the design and realisation of an optimised vein image capture device is presented. In order to maximise the quality of the captured images with relatively low cost, the infrared illumination and imaging theory are discussed. Then a database containing 2040 images from 102 individuals, which were captured by this device, is introduced. Secondly, image analysis and the customised image pre-processing methods are discussed. The consistency of the database images is evaluated using mean squared error (MSE) and peak signal-to-noise ratio (PSNR). Geometrical pre-processing, including shearing correction and region of interest (ROI) extraction, is introduced to improve image consistency. Image noise is evaluated using total variance (TV) values. Grey-level pre-processing, including grey-level normalisation, filtering and adaptive histogram equalisation are applied to enhance vein patterns. Thirdly, a gradient-based image segmentation algorithm is compared with popular algorithms in references like Niblack and Threshold Image algorithm to demonstrate its effectiveness in vein pattern extraction. Post-processing methods including morphological filtering and thinning are also presented. Fourthly, feature extraction and recognition methods are investigated, with several new approaches based on keypoints and local binary patterns (LBP) proposed. Through comprehensive comparison with other approaches based on structure and texture features as well as performance evaluation using the database created with 2040 images, the proposed approach based on multi-scale partition LBP is shown to provide the best recognition performance with an identification rate of nearly 99%. Finally, the whole hand-dorsa vein identification system is presented with a user interface for administration of user information and for person identification