04-10-2016, 10:51 AM
1457606329-finalconf.docx (Size: 360.58 KB / Downloads: 11)
Multibiometrics can provide higher identification accuracy than single biometrics, so it is more suitable for some real-world personal identification applications that need high-standard security. Among various biometrics technologies, palmprint identification has received much attention because of its good performance. Combining the left and right palmprint images to perform multibiometrics is easy to implement and can obtain better results. However, previous studies did not explore this issue in depth. In this paper, we proposed a novel framework to perform multibiometrics by comprehensively combining the left and right palmprint images. This framework integrated three kinds of scores generated from the left and right palmprint images to perform matching score-level fusion. The first two kinds of scores were, respectively, generated from the left and right palmprint images and can be obtained by any palm print identification method, whereas the third kind of score was obtained using a specialized algorithm proposed in this paper. As the proposed algorithm carefully takes the nature of the left and right palmprint images into account, it can properly exploit the similarity of the left and right palmprints of the same subject. Moreover, the proposed weighted fusion scheme allowed perfect identification performance to be obtained in comparison with previous palmprint identification methods.
Palm print recognition inherently implements many of the same matching characteristics that have allowed fingerprint recognition to be one of the most well-known and best publicized biometrics. Both palm and finger biometrics are represented by the information presented in a friction ridge impression. This information combines ridge flow, ridge characteristics, and ridge structure of the raised portion of the epidermis. The data represented by these friction ridge impressions allows a determination that corresponding areas of friction ridge impressions either originated from the same source or could not have been made by the same source. Because fingerprints and palms have both uniqueness and permanence, they have been used for over a century as a trusted form of identification. However, palm recognition has been slower in becoming automated due to some restraints in computing capabilities and live-scan technologies.
Palm recognition technology exploits some of these palm features. Friction ridges do not always flow continuously throughout a pattern and often result in specific characteristics such as ending ridges or dividing ridges and dots. A palm recognition system is designed to interpret the flow of the overall ridges to assign a classification and then extract the minutiae detail — a subset of the total amount of information available, yet enough information to effectively search a large repository of palm prints. Minutiae are limited to the location, direction, and orientation of the ridge endings and bifurcations (splits) along a ridge path.
The images in Figure present a pictorial representation of the regions of the palm, two types of minutiae, and examples of other detailed characteristics used during the automatic classification and minutiae extraction processes.
Palmprint identification is an important personal identification technology and it has attracted much attention. The palmprint contains not only principle curves and wrinkles but also rich texture and miniscule points, so the palmprint identification is able to achieve a high accuracy because of available rich information in palmprint. Various palmprint identification methods, such as coding based methods and principle curve methods have been proposed in past decades. In addition to these methods, subspace based methods can also perform well for palmprint identification. For example, Eigen palm and Fisher palm are two well-known subspace based palmprint identification methods.
In recent years, 2D appearance based methods such as 2D Principal Component Analysis (2DPCA), 2D Linear Discriminant Analysis (2DLDA), and 2D Locality Preserving Projection (2DLPP) have also been used for palmprint recognition. Further, the Representation Based Classification (RBC) method also shows good performance in palmprint identification. Additionally, the Scale Invariant Feature Transform (SIFT) which transforms image data into scale-invariant coordinates, are successfully introduced for the contactless palmprint identification.
Extensive experiments show that the proposed framework can integrate most conventional palmprint identification methods for performing identification and can achieve higher accuracy than conventional methods. This work has the following notable contributions. First, it for the first time shows that the left and right palmprint of the same subject are somewhat correlated, and it demonstrates the feasibility of exploiting the crossing matching score of the left and right palmprint for improving the accuracy of identity identification. Second, it proposes an elaborated framework to integrate the left palmprint, right palmprint, and crossing matching of the left and right palmprint for identity identification. Third, it conducts extensive experiments on both touch-based and contactless palmprint databases to verify the proposed framework.
In biometrics there are two types of identity matching: identification and verification. Identification is a one-to-many comparison of an individual’s biometric sample against a template database of previously gathered samples.
Verification refers to a one-to-one comparison between a previously acquired template of an individual and a sample which we want to authenticate. An application providing verification support would also require someother means for the user to claim his identity (e.g. information contained in a smart card, keyboard for user input), while for identification purpose this is not needed.
Palmprint recognition uses the person‘s palm as a bio-metric for identifying or verifying person’s identity. Palmprint patterns are a very reliable biometric and require minimum cooperation from the user for extraction.
Palmprint is distinctive, easily captured by low resolution devices as well as contains additional features such as principal lines, wrinkles and ridges. Therefore it is suitable
for everyone and it does not require any personal information of the user. Palm normally contains three flexion creases (principal lines), secondary creases (wrinkles) and ridges. The three major flexions are genetically dependent; most of other creases are not. Even identical twins have different palmprints. These non-genetically deterministic and complex patterns are very useful in personal identification. Palm is the inner surface of the hand between the wrist and fingers. Palm area contains large number of features such as principle lines, wrinkles, minutiae, datum point features and texture images. Most of the system uses the low resolution image.
EXISTING SYSTEM:
Palmprint identification is an important personal iden-tification technology and it has attracted much attention. The palmprint contains not only principle curves and wrinkles but also rich texture and miniscule points, so the palmprint identification is able to achieve a high accuracy because of available rich information in palmprint .
Various palmprint identification methods, such as coding based methods and principle curve methods have been proposed in past decades. In recent years, 2D appearance based methods such as 2D Principal Component Analysis (2DPCA) , 2D Linear Discriminant Analysis (2DLDA) , and 2D Locality Preserving Projection (2DLPP) have also been used for palmprint recognition.
EXISTING METHODS:
A. Line Based Method
Lines are the basic feature of palmprint and line based methodes play an important role in palmprint verification and identification. Line based methods use lines or edge detectors to extract the palmprint lines and then use them to perform palmprint verification and identification. In general, most palms have three principal lines: the heartline, headline, and lifeline, which are the longest and widest lines in the palmprint image and have stable line shapes and positions.
Thus, the principal line based method is able to provide stable performance for palmprint verification. Palmprint principal lines can be extracted by using the Gobor filter, Sobel operation, or morphological operation.
Coding based method
Coding based methods are the most influential palm-print identification methods. Representative coding based methods include the competitive code method, ordi-nal code method, palmcode method and Binary Orientation Co-occurrence Vector (BOCV) method , and so on.
C. Subspace Based Methods
Subspace based methods include the PCA, LDA, and ICA etc. The key idea behind PCA is to find an orthogonal subspace that preserves the maximum variance of the original data. The PCA method tries to find the best set of all samples by using the following objective function:
JPC A=arg maxW|WTStW|
whereSt is the total scatter matrix of the training samples, and W is the projection matrix whose columns are orthonormal vectors. PCA chooses the first few principal components and uses them to transform the samples in to a low-dimensional feature space.
LDA tries to find an optimal projection matrixWand transforms the original space to a lower-dimensional feature space. The goal of LDA is to maximize the ratio of the
between-class distance against within-class distance which is defined as:
JLDA=arg maxW|WTSbW|/|WTSwW|
Where Sb is the between-class scatter matrix, and Sw is the within-class scatter matrix. In the subspace palmprint identification method, the query palmprint image is usually classified into the class which produces the minimum Euclidean distance with the query sample in the low-dimensional feature space.