Finger-vein pattern identification using principal component analysis and the neural network technique

This paper presents a personal identification system using finger-vein patterns with component analysis and neural network technology. In the proposed system, the finger-vein patterns are captured by a device that can transmit near infrared through the finger and record the patterns for signal analysis. The proposed biometric system for verification consists of a combination of feature extraction using principal component analysis (PCA) and pattern classification using back-propagation (BP) network and adaptive neuro-fuzzy inference system (ANFIS). Finger-vein features are first extracted by PCA method to reduce the computational burden and removes noise residing in the discarded dimensions. The features are then used in pattern classification and identification. To verify the effect of the proposed ANFIS in the pattern classification, the BP network is compared with the proposed system. The experimental results indicated the proposed system using ANFIS has better performance than the BP network for personal identification using the finger-vein patterns.     

Finger-vein verification based on the curvature in Radon space

Finger-vein verification has drawn increasing attention because it is highly secured and private biometric in practical applications. However, as the imaging environment is affected by many factors, the captured image contains not only the vein pattern but also the noise and irregular shadowing which can decrease the verification accuracy. To address this problem, in this paper, we proposed a new finger-vein extraction approach which detects the valley-like structures using the curvatures in Radon space. Firstly, given a pixel, we obtain eight patches centered on it by rotating a window along eight different orientations and project the resulting patches into Radon space using the Radon transform. Secondly, the vein patches create prominent valleys in Radon space. The vein patterns are enhanced according to the curvature values of the valleys. Finally, the vein network is extracted from the enhancing image by a binarization scheme and matched for personal verification. The experimental results on both contacted and contactless finger-vein databases illustrate that our approach can significantly improve the accuracy of the finger-vein verification system.     

Finger-vein pattern identification using SVM and neural network technique

This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98% and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system.     

Personal identification based on finger-vein features

Finger-vein recognition refers to a recent biometric technique which exploits the vein patterns in the human finger to identify individuals. The advantages of finger vein over traditional biometrics (e.g. face, fingerprint, and iris) lie in low-risk forgery, noninvasiveness, and noncontact. This paper here presents a new method of personal identification based on finger-vein recognition. First, a stable region representing finger-vein network is cropped from the image plane of an imaging sensor. A bank of Gabor filters is then used to exploit the finger-vein characteristics at different orientations and scales. Based on the filtered image, both local and global finger-vein features are extracted to construct a finger-vein code (FVCode). Finally, finger-vein recognition is implemented using the cosine similarity measure classifier, and a fusion scheme in decision level is adopted to improve the reliability of identification. Experimental results show that the proposed method exhibit an exciting performance in personal identification.     

An automotive generator fault diagnosis system using discrete wavelet transform and artificial neural network

This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.     

A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network

An expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and neural networks is presented in this paper. The order tracking technique is one of the important approaches for fault diagnosis in rotating machinery. The different faults present different order figures and they can be used to determine the fault in mechanical systems. However, many breakdowns are hard to classify correctly by human experience in fault diagnosis. In the present study, the order tracking problem is treated as a parametric identification and the artificial neural network technique for classifying faults. First, the adaptive order tracking extract the order features as input for neural network in the proposed system. The neural networks are used to develop the training module and testing module. The artificial neural network techniques using a back-propagation network and a radial basis function network are proposed to develop the artificial neural network for fault diagnosis system. The performance of two techniques are evaluated and compared through experimental investigation. The experimental results indicated that the proposed system is effective for fault diagnosis under various engine conditions.     

Finger-vein recognition based on parametric-oriented corrections

The two key factors in a biometric identification system are its high identification rate and convenience of device usage. In a finger-vein identification task, these two problems often occur since the captured device of finger-vein image should accommodate the high identification rate as well as the easy-to-use device design. The finger-vein is visually invisible inside the human skin. This work develops a new finger-vein capturing device using Near-Infrared (NIR) LED light and proposes an efficient technique for finger-vein identification. The vein image may contain noise and shadows due to device lighting conditions. Parametric-Oriented Histogram Equalization (POHE) is utilized to enhance image contrast and reduce the noise effect. This work also discusses normalized issues related to the angle correction of the finger edge and Region of Interest (ROI) for width normalization. In the experimental result, the proposed method yields a clear finger-vein pattern with a superior identification rate in the recognition task compared to the state-of-the-art methods.

     

Faulted gear identification of a rotating machinery based on wavelet transform and artificial neural network

In this paper, a condition monitoring and faults identification technique for rotating machineries using wavelet transform and artificial neural network is described. Most of the conventional techniques for condition monitoring and fault diagnosis in rotating machinery are based chiefly on analyzing the difference of vibration signal amplitude in the time domain or frequency spectrum. Unfortunately, in some applications, the vibration signal may not be available and the performance is limited. However, the sound emission signal serves as a promising alternative to the fault diagnosis system. In the present study, the sound emission of gear-set is used to evaluate the proposed fault diagnosis technique. In the experimental work, a continuous wavelet transform technique combined with a feature selection of energy spectrum is proposed for analyzing fault signals in a gear-set platform. The artificial neural network techniques both using probability neural network and conventional back-propagation network are compared in the system. The experimental results pointed out the sound emission can be used to monitor the condition of the gear-set platform and the proposed system achieved a fault recognition rate of 98% in the experimental gear-set platform.     

Fake finger-vein image detection based on Fourier and wavelet transforms

Recently, finger-vein recognition has received considerable attention. It is widely used in many applications because of its numerous advantages, such as the small capture device, high accuracy, and user convenience. Nevertheless, finger-vein recognition faces a number of challenges. One critical issue is the use of fake finger-vein images to carry out system attacks. To overcome this problem, we propose a new fake finger-vein image-detection method based on the analysis of finger-vein images in both the frequency and spatial domains.This research is novel in five key ways. First, very little research has been conducted to date on fake finger-vein image detection. We construct a variety of fake finger-vein images, printed on A4 paper, matte paper, and overhead projector film, with which we evaluate the performance of our system. Second, because our proposed method is based on a single captured image, rather than a series of successive images, the processing time is short, no additional image alignment is required, and it is very convenient for users. Third, our proposed method is software-based, and can thus be easily implemented in various finger-vein recognition systems without special hardware. Fourth, Fourier transform features in the frequency domain are used for the detection of fake finger-vein images; further, both spatial and frequency characteristics from Haar and Daubechies wavelet transforms are used for fake finger-vein image detection. Fifth, the detection accuracy of fake finger-vein images is enhanced by combining the features of the Fourier transform and Haar and Daubechies wavelet transforms based on support vector machines.Experimental results indicate that the equal error rate of fake finger-vein image detection with our proposed method is lower than that with a Fourier transform, wavelet transform, or other fusion methods.     

Finger-vein authentication based on deformation-tolerant feature-point matching

A novel method for finger-vein authentication based on feature-point matching is proposed and evaluated. A finger-vein image captured by infrared light contains artifacts such as irregular shading and vein posture deformation that can degrade accuracy of finger-vein authentication. Therefore, a method is proposed for extracting features from vein patterns and for matching feature points that is robust against irregular shading and vein deformation. In the proposed method, curvature of image-intensity profiles is used for feature point extraction because such image profiles are a robust feature against irregular shading. To increase the number of feature points, these points are extracted from any positions where vein shape is non-linear. Moreover, a finger-shape model and non-rigid registration method are proposed. Both the model and the registration method correct a deformation caused by the finger-posture change. It is experimentally shown that the proposed method achieves more robust matching than conventional methods. Furthermore, experiments on finger-vein identification show that the proposed method provides higher identification accuracy than conventional methods.     

Speaker identification using discrete wavelet packet transform technique with irregular decomposition

This paper presents the study of speaker identification for security systems based on the energy of speaker utterances. The proposed system consisted of a combination of signal pre-process, feature extraction using wavelet packet transform (WPT) and speaker identification using artificial neural network. In the signal pre-process, the amplitude of utterances, for a same sentence, were normalized for preventing an error estimation caused by speakers’ change in volume. In the feature extraction, three conventional methods were considered in the experiments and compared with the irregular decomposition method in the proposed system. In order to verify the effect of the proposed system for identification, a general regressive neural network (GRNN) was used and compared in the experimental investigation. The experimental results demonstrated the effectiveness of the proposed speaker identification system and were compared with the discrete wavelet transform (DWT), conventional WPT and WPT in Mel scale.     

Driving Profile Modeling and Recognition Based on Soft Computing Approach

Advancements in biometrics-based authentication have led to its increasing prominence and are being incorporated into everyday tasks. Existing vehicle security systems rely only on alarms or smart card as forms of protection. A biometric driver recognition system utilizing driving behaviors is a highly novel and personalized approach and could be incorporated into existing vehicle security system to form a multimodal identification system and offer a greater degree of multilevel protection. In this paper, detailed studies have been conducted to model individual driving behavior in order to identify features that may be efficiently and effectively used to profile each driver. Feature extraction techniques based on Gaussian mixture models (GMMs) are proposed and implemented. Features extracted from the accelerator and brake pedal pressure were then used as inputs to a fuzzy neural network (FNN) system to ascertain the identity of the driver. Two fuzzy neural networks, namely, the evolving fuzzy neural network (EFuNN) and the adaptive network-based fuzzy inference system (ANFIS), are used to demonstrate the viability of the two proposed feature extraction techniques. The performances were compared against an artificial neural network (NN) implementation using the multilayer perceptron (MLP) network and a statistical method based on the GMM. Extensive testing was conducted and the results show great potential in the use of the FNN for real-time driver identification and verification. In addition, the profiling of driver behaviors has numerous other potential applications for use by law enforcement and companies dealing with buses and truck drivers.     

Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification

We propose a method of personal identification based on finger-vein patterns. An image of a finger captured under infrared light contains not only the vein pattern but also irregular shading produced by the various thicknesses of the finger bones and muscles. The proposed method extracts the finger-vein pattern from the unclear image by using line tracking that starts from various positions. Experimental results show that it achieves robust pattern extraction, and the equal error rate was 0.145% in personal identification.Received: 27 October 2003, Accepted: 25 February 2004, Published online: 21 July 2004     

对抗图像旋转的手指静脉身份识别新算法的设计

为了克服图像旋转对手指静脉身份识别系统正确率的影响,在图像预处理部分提出一种基于手指指尖点的旋转定位方法,改进了基于方向模板和局部动态阈值分割提取静脉特征的方法并用改进Hausdorff距离（MHD）距离进行匹配验证。实验结果表明,同一根手指的图片在平面偏移角度小于20°时,可以达到0.75%的等误率,正确识别率达97.25%,而且整个处理过程在VC++6.0上面执行耗时仅为161.6949ms,系统具有很好的实时性能,对实际手指静脉身份识别产品的开发具有一定的现实意义。     

Fingerprint matching using multi-dimensional ANN

Fingerprint matching is one of the most widely used biometric technique for personal identification. This identification is achieved in this work by using the concept that every fingerprint has a unique pattern of distribution of the minutiae points. In this paper, a new method of recognition of this pattern of distribution of the minutiae points of an enhanced image is considered by using a multi-dimensional artificial neural network (MDANN). The proposed technique has the distinct advantage of using the entire resized minutiae image as an input at once. It is capable of excellent pattern recognition properties as the distribution of the minutiae points are used directly for recognition. The proposed approach shows significant promise and potential for improvement, compared with the other conventional matching techniques with regard to time and efficiency of results.     

Special point representations for reducing data space requirements of finger-vein recognition applications

Due to the uniqueness of the finger-vein patterns hidden beneath the skin, forgery is very difficult. Providing fast and accurate finger-vein recognition represents the answer to biometric security system as we need more secure and reliable authentication methods. However, the finger-vein based recognition system is limited by the storage space and time complexity, which significantly reduce the accuracy of the identification. In this paper, we present an effective method of matching in a finger-vein recognition system to overcome the disadvantage of requiring significant data storage and heavy CPU computation requirements. Our proposed solution involved considering special points characterizing complex finger-vein information and their connections, thereby retaining only the evidence related to matching to perform subsequent identification. Experimental results show that our method achieves robust matching with an error rate of 0.216 % and confirm that the proposed mechanism can reduce the quantity of data that requires storage and maintain a certain level of authentication accuracy.     

利用Radon变换实现手写数字识别的新方法

提出了一种新的手写数字识别方法,利用传统的Radon变换,找到了一种新的旋转不变特征,最后采用BP神经网络分类器进行分类。实验表明,该方法不仅具有93.89%的高识别率,而且对字符旋转具有很好的鲁棒性。     

An optimum feature extraction method based on Wavelet–Radon Transform and Dynamic Neural Network for pavement distress classification

Quantification of pavement crack data is one of the most important criteria in determining optimum pavement maintenance strategies. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. This paper present an automatic diagnosis system for detecting and classification pavement crack distress based on Wavelet–Radon Transform (WR) and Dynamic Neural Network (DNN) threshold selection. The algorithm of the proposed system consists of a combination of feature extraction using WR and classification using the neural network technique. The proposed WR + DNN system performance is compared with static neural network (SNN). In test stage; proposed method was applied to the pavement images database to evaluate the system performance. The correct classification rate (CCR) of proposed system is over 99%. This research demonstrated that the WR + DNN method can be used efficiently for fast automatic pavement distress detection and classification. The details of the image processing technique and the characteristic of system are also described in this paper.     

基于自组特征映射神经网络的虹膜识别算法研究

虹膜识别是一种非侵犯性的身份鉴别技术。本文讨论了虹膜图像定位、归一化和增强等预处理方法,并提出了一种基于神经元网络的虹膜图像识别算法,它是对提取的归一化矩形虹膜纹理数据用训练的自组织特征映射(SOM)神经网络进行虹膜模式识别。实验表明,该方法识别正确率高,效果良好。     

Brain tumor diagnosis based on artificial neural network and a chaos whale optimization algorithm

Accurate and early detection of the brain tumor region has a great impact on the choice of treatment, its success rate, and the follow-up of the disease process over time. This study presents a new bioinspired technique for the early detection of the brain tumor area to improve the chance of completely healing. The study presents a multistep technique to detect the brain tumor area. Herein, after image preprocessing and image feature extraction, an artificial neural network is used to determine the tumor area in the image. The method is based on using an improved version of the whale optimization algorithm for optimal selection of the features and optimizing the artificial neural network weights for classification. Simulation results of the proposed method are applied to FLAIR, T1, and T2 datasets and are compared with different algorithms. Three performance indexes including correct detection rate, false acceptance rate, and false rejection rate are selected for the system performance analysis. Final results showed the superiority of the proposed method toward the other similar methods.