Iris Recognition System Using Wavelet Packet and Support Vector Machines

An original dual-class voting mechanism was put forward as the final decision method for brain computer interface, which is based on wavelet packet decomposition (WPD) to extract the brainwave features from EEG signals, and support vector machines (SVMs) to classify five mental tasks. Moreover, several preprocessing methods were applied efficiently. Segmentation along the time axis for increasing the correct classification rate, and nonlinear as well as linear normalization for emphasizing the important information in small magnitude and optimizing data distribution. Further, an especial grouping method was proposed to realize optimizing parameters automatically. Approximately, 95% of correct classification rate is obtained based on the proposed method, which is higher than the conventional.

— Iris recognition system provides an approach for individual identification and is regarded as the sophisticated biometric identification system. Therefore, the exclusive features of iris patterns should be extracted and processed. In this paper, a new feature extraction method according to ridgelet transform for identifying the iris images is provided. At first, after segmentation and normalization the collarette area of iris images has been extracted. Then we improve the quality of image by using median filter, histogram equalization, and the two-dimensional (2-D) Wiener filter as well. Finally, ridgelet transform is employed for extracting features and then, the binary bit stream vector is generated. The Hamming distance (HD) between the input bit stream vector and stored vectors is calculated for iris identification. The experimental results show efficiency of the proposed method. Keywords- Iris identification, ridgelet transform. I. I NTRODUCTION Biometrics is the technology of identification a person from a physical characteristic and it is based on behavioral characteristics of human structures. Among existing different structural features, it was shown that exclusive features like iris pattern have very suitable features [1]. Some properties of the human iris that enhance its suitability for automatic identification include: 1) its inherent isolation and protection from the external environment, being an internal organ of the eye, behind the cornea and the aqueous humor 2) the impossibility of surgically modifying it without high risk of damaging the user’s vision 3) its physiological response to light, which provides the detection of a dead or plastic iris and 4) never are there two irises alike, even for identical twin. Many researchers developed iris recognition systems. Daugman [2] proposed an algorithm which was based on iris codes. Integral differential operators are used to detect the centre and diameter of the iris. The image is converted from cartesian to polar transform and rectangular representation of the region of interest is generated. Feature extraction algorithm uses the complex valued 2-D Gabor wavelets to generate the iris codes which are then matched using HDs. Boles and Boashash [3] used zero crossing point of 1-D wavelet at different levels on same center circle of the iris and pupil centralism. Wildes [4] used Laplace pyramid with four various resolution levels to range and normalize correlation for comparison between input image and database images. Ma et al. [5] used 4 dimensional Haar wavelet analysis in four steps decomposition. Krichen et al. [6] used wavelet packets to produce an iris code at each

Iris is an evenly highlighted radial membrane with complex patterns that are perceptible upon near inspection, which exists behind the cornea of the eye with a changeable circular opening called the pupil. Iris Recognition System is a technique of identifying people using those complex distinctive features in patterns. Generally, Iris recognition system is used in security allied applications such as, authenticating PCs, network and mobile devices, physical and logical premises access control, national border control, secure banking and financial transactions, national identity like AADHAAR in India etc. This paper reviews the state-of-art design and implementation of various iris recognition systems. The contributions of the paper include: 1) conferring the importance, applications and deployment of iris recognition system related to human identification; 2) providing an analysis on iris recognition methods in effect; 3, discussing the present research defies; 4) providing commendations for the future research on iris recognition system.

We proposed a new method of aero-engine early fault intelligent diagnosis which combined with stochastic resonance, wavelet packet analysis and support vector machine. This method can effectively extract the early fault feature of aero-engine and it can fast identify the early faults. At first, we use the principle of stochastic resonance to zooms the early weak fault feature signals and amplify fault features. Then, we make use of multi-resolution analysis characteristic of wavelet packet to extract the early fault feature vectors. At last, the feather vector is inputted to a classifier which is constructed by support vector machines and carries on identification of the early faults. The results shown that its effect of classification identification is well and it is effective to identify early fault in strong noise.

Among biometrics, iris and ocular recognition systems are the methods that recognize eye features in an image. Such iris and ocular regions must have a certain image resolution to achieve a high recognition performance; otherwise, the risk of performance degradation arises. This is even more critical in the case of iris recognition where detailed patterns are used. In cases where such low-resolution images are acquired and the acquisition apparatus and environment cannot be improved, recognition performance can be enhanced by obtaining high-resolution images with methods such as super-resolution reconstruction. However, previous survey papers have mainly summarized studies on high-resolution iris and ocular recognition, but do not provide detailed summaries of studies on low-resolution iris and ocular recognition. Therefore, we investigated high-resolution iris and ocular recognition methods and introduced in detail the low-resolution iris and ocular recognition methods and methods of solving the low-resolution problem. Furthermore, since existing survey papers have focused on and summarized studies on traditional handcrafted feature-based iris and ocular recognition, this survey paper also introduced the latest deep learning-based methods in detail.

Wavelet neural networks (WNN) and support vector machine (SVM) are two advanced methods which are fit for classification. A comparative analysis of the two methods was conducted based on Chinese firms. The results show WNN has good classification effect. Wavelet decomposition has been demonstrated to be an effective tool for recognizing the firms' feature. Also the study applied SVM to the same estimation sample and test sample. The results show SVM is much superior to WNN for small sample learning.

A new forecasting method of wind speed based on wavelet analysis and support vector machine is proposed in this paper. By the method, firstly, the original wind speed sequences are decomposed into coarse components and detail components, and secondly, every wavelet components are separately forecasted with corresponding support vector machine models. Finally, the forecasting results of original wind speed series are achieved by using wavelet reconstruction. The experimental results by simulation prove that this method is capable of improving generalization performance and forecasting precision. The mean absolute percentage error (MAPE) reaches 8.21%.

The iris is a stable biometric trait that has been widely used for human recognition in various applications. However, deployment of iris recognition in forensic applications has not been reported. A primary reason is the lack of human-friendly techniques for iris comparison. To further promote the use of iris recognition in forensics, the similarity between irises should be made visualizable and interpretable. Recently, a human-in-the-loop iris recognition system was developed, based on detecting and matching iris crypts. Building on this framework, we propose a new approach for detecting and matching iris crypts automatically. Our detection method is able to capture iris crypts of various sizes. Our matching scheme is designed to handle potential topological changes in the detection of the same crypt in different images. Our approach outperforms the known visible-feature-based iris recognition method on three different data sets. In particular, our approach achieves over 22% higher rank one hit rate in identification, and over 51% lower equal error rate in verification. In addition, the benefit of our approach on multi-enrollment is experimentally demonstrated.

The automatic pattern recognition of power network fault is important work for power network fault diagnosis in power system. This paper presents automatic pattern recognition methods based on wavelet packet time entropy and support vector machine (SVM) for distribution network operation states. The proposed methods employ wavelet packet time entropy to extract the most important features in the current when the faults happen in the power network. Then the type of power network fault is identified by SVM. The proposed method is found the most suitable pattern recognition tool in the power network fault diagnosis by simulations in typical distribution network. The simulation results show that the method has good performance on pattern recognition with low dimension of eigenvector, less training samples and high classification accuracy.

There is an important military significance about aircrafts type recognition based on shortwave communication. Acoustic signals received by shortwave radio about aircrafts are complex and they contain some valuable information. It is very difficult to identify the aircraft type based on background sound signals of shortwave communication. In order to solve this problem, in this paper two methods for aircrafts type recognition based on shortwave communication are presented. The acoustic signals characteristics of aircrafts are extracted by wavelet packet decomposition (WPD). Support vector machine (SVM) and back propagation (BP) artificial neural network (ANN) as classifiers are adopted to identify five kinds of aircrafts respectively. From results of experiments about classification and recognition of five kinds of aircrafts, the combination of WPD as feature extraction method and SVM as the classifier has better performance and recognition rate than the combination of WPD and BP ANN.

Recent research works have revealed that it is not difficult to spoof an automated iris recognition system using fake iris such as contact lens and paper print etc. Therefore, it is very important to detect fake iris as much as possible. In this paper, we propose a new fake iris detection method based on wavelet packet transform. First, wavelet packet decomposition is used to extract the feature values which provide unique information for discriminating fake irises from real ones. Second, to enhance the detecting accuracy of fake iris, Support vector machine (SVM) is used to characterize the distribution boundary based on extracted wavelet packet features, for it has good classification performance in high dimensional space and it is originally developed for two-class problems. The experimental results indicate the proposed method is to be a very promising technique for making iris recognition systems more robust against fake iris spoofing attempts.

Partial discharge is an important cause of the deterioration of the cable insulation condition, and also an important manifestation of the deteriorated insulation condition. Partial discharge detection for cable insulation is an important means to find the cable insulation defects and assess state of the cable insulation. Partial discharge signals from different sources have great harm to the power equipment, so pattern identification is an important purpose in this research. First, the four kinds of discharge waveforms are processed by de-noising preprocessing, then discharge waveforms are decomposed by wavelet packet, and finally the energy percentage in different frequency bands can be obtained. Studies show that when the type of discharge signal is different, the wavelet packet coefficients in the distribution of each band is also different. So energy percentage of the wavelet packet coefficients can be used for discharge signal feature recognition. In this paper, SVM binary classification is first extended to multi-classification by M-ary algorithm, then the feature vectors composed by energy percentage of the wavelet packet coefficients are inputted into SVM for recognition. The recognition results of test samples show that feature vectors composed by the percentage of energy of wavelet packet coefficients can well reflect the characteristics of the original signal, and the method based on SVM has better recognition performance.

The use of iris as biometric trait has emerged as one of the most preferred method because of its uniqueness, lifetime stability and regular shape. Moreover it shows public acceptance and new user-friendly capture devices are developed and used in a broadened range of applications. Currently, iris recognition systems work well with frontal iris images from cooperative users. Nonideal iris images are still a challenge for iris recognition and can significantly affect the accuracy of iris recognition systems. In this paper, we propose a method to correct off-angle iris image. Taking into account the eye morphology and the reflectance properties of the external transparent layers, we can evaluate the distorting effect that is present in the acquired image. The correction algorithm proposed includes a first modeling phase of the human eye, a segmentation of the acquired image, and a simulation phase where the acquisition geometry is reproduced and the distortions are evaluated. Finally we obtain an image which does not contain the distorting effects due to jumps in the refractive index. We show how this correction process reduce the intra-class variations for off-angle iris images.

Among all biometric technologies, iris recognition is the most accurate and high confidence authentication system. Due to the limitations in PCA-based system, modified principal component analysis (PCA)-based feature extraction is proposed in iris recognition system. In the proposed system, features are extracted using Gaussian kernel PCA (GKPCA) and classified using support vector machine (SVM). GKPCA and SVM algorithms are evaluated using CASIA V3 iris database. The performances are compared with the existing PCA-based system. The proposed system achieves 96.67% of accuracy for 256 features using GKPCA linear SVM. False acceptance rate (FAR) and false rejection rate (FRR) are 0 and 3 respectively for linear SVM. The results show that the proposed system performs accurate localisation of patterns even in non-ideal conditions.

Based on wavelet packet decomposition and conditions of the support vector kernel function, a non-linear wavelet basis is introduced to construct the kernel function of support vector machine (SVM), and a tighten wavelet support vector machine (WSVM), which has strong generalization ability is obtained. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction according to wavelet energy spectrum. The feature vectors are used for training and classification as the inputs of the tighten SVM. Hence, a new structural damage detection method called complete WSVM is established. This method is used to a single-layer spherical lattice dome for damage detection. The structural damage location and degree can be detected and classified, and the result is highly accurate. This approach has some advantages, such as engineering oriented, low cost and convenient. Copyright © 2005 John Wiley & Sons, Ltd.