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In recent years, palmprint based personal identification has been extensively explored by researchers. The success of this technology has demonstrated that the inner part of palm skin is capable of distinguishing one person from another in case that proper representation is utilized. However, earlier work mainly focused on scenarios where the position and pose of hands are constrained by pegs or plates. In contrast, our purpose is to design and implement a system which is capable of recognizing an individual once he/she naturally stretches his/her hand in front of the camera. Since human hand is an articulated object, it is important to filter out geometry variations. This paper presents and compares two hand texture based personal identification methods, which are called hand-print verification in this paper to denote the idea of utilizing whole hand skin image for recognition. In one of the method, hand articulation is eliminated in a well-defined way and then the hand is treated as a whole for feature extraction, while in the other method, features are extracted in different parts of the hand, and final decision is made in a matching score level fusion manner. Experimental results for both methods are presented and compared. [ABSTRACT FROM AUTHOR]

Iris-based biometric recognition outperforms other biometric methods in terms of accuracy. In this paper an iris normalization model for iris recognition is proposed, which combines linear and non-linear methods to unwrap the iris region. First, non-linearly transform all iris patterns to a reference annular zone with a predefined λ, which is the ratio of the radii of inner and outer boundaries of the iris. Then linearly unwrap this reference annular zone to a fix-sized rectangle block for subsequence processing. Our iris normalization model is illuminated by the ‘minimum-wear-and-tear' meshwork of the iris and it is simplified for iris recognition. This model explicitly shows the non-linear property of iris deformation when pupil size changes. And experiments show that it does better than the over-simplified linear normalization model and will improve the iris recognition performance. [ABSTRACT FROM AUTHOR]

Iris recognition is one of the most reliable methods for personal identification. However, not all the iris images obtained from the device are of high quality and suitable for recognition. In this paper, a novel approach for iris image quality assessment is proposed to select clear images in the image sequence. The proposed algorithm uses three distinctive features to distinguish three kinds of poor quality images, i.e. defocus, motion blur and occlusion. Experimental results demonstrate the effectiveness of the algorithm. Clear iris images selected by our method are essential to subsequent iris recognition. [ABSTRACT FROM AUTHOR]

Iris pattern is commonly regarded as a kind of phenotypic feature without relation to the genes. In this paper, we propose a novel ethnic classification method based on the global texture information of iris images. So we would argue that iris texture is race related, and its genetic information is illustrated in coarse scale texture features, rather than preserved in the minute local features of state-of-the-art iris recognition algorithms. In our scheme, a bank of multichannel 2D Gabor filters is used to capture the global texture information and AdaBoost is used to learn a discriminant classification principle from the pool of the candidate feature set. Finally iris images are grouped into two race categories, Asian and non-Asian. Based on the proposed method, we get an encouraging correct classification rate (CCR) of 85.95% on a mixed database containing 3982 iris samples in our experiments. [ABSTRACT FROM AUTHOR]

Iris-based personal identification has attracted much attention in recent years. Almost all the state-of-the-art iris recognition algorithms are based on statistical classifier and local image features, which are noise sensitive and hardly to deliver perfect recognition performance. In this paper, we propose a novel iris recognition method, using the histogram of local binary pattern for global iris texture representation and graph matching for structural classification. The objective of our idea is to complement the state-of-the-art methods with orthogonal features and classifier. In the texture-rich iris image database UPOL, our method achieves higher discriminability than state-of-the-art approaches. But our algorithm does not perform well in the CASIA database whose images are less textured. Then the value of our work is demonstrated by providing complementary information to the state-of-the-art iris recognition systems. After simple fusion with our method, the equal error rate of Daugman's algorithm could be halved. [ABSTRACT FROM AUTHOR]

In this paper, we present an ordinal feature based method for face recognition. Ordinal features are used to represent faces. Hamming distance of many local sub-windows is computed to evaluate differences of two ordinal faces. AdaBoost learning is finally applied to select most effective hamming distance based weak classifiers and build a powerful classifier. Experiments demonstrate good results for face recognition on the FERET database, and the power of learning ordinal features for face recognition. [ABSTRACT FROM AUTHOR]

Selecting appropriate thresholds and fusion rules for a system involving multiple biometric verifiers requires knowledge of the match score statistics for each verifier. While this statistical information can often be measured from training data, that data may not be representative of the environment into which each verifier is deployed. To compensate for missing statistics, we present a technique for estimating the error rates of each verifier using decisions made after a system has been deployed. While this post-deployment data lacks class labels, it is guaranteed to be representative. Extracted error rates can be used to select appropriate fusion rules and search for thresholds that meet operational requirements. [ABSTRACT FROM AUTHOR]

A multimodal biometric scheme using watermarking technique to provide more secure and reliable personal recognition is proposed in this paper. Two distinct biometric traits have been under consideration: palmprint and knuckleprint. The palmprint image is chosen to be the host image. Knuckleprint biometric feature is selected to use as watermark hidden in the host image. Such that knuckleprint watermark not only protects palmprint biometric data, but also can be used as a covert recognition. Meanwhile, the bimodal biometrics recognition provides the improvement in the accuracy performance of the system. The experiment results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we present a new biometric identification system based on dental features. First, we collected the plaster figures of teeth which were done at dental hospital, department of oral medicine. Second, we developed a representation of dental images based on principal component analysis(PCA) representation included the 100 principle components as the features for individual identification. The PCA basis vectors had reflected well the features for individual identification from the parts of teeth. Finally, the classification for individual identification based on the nearest neighbor(NN) algorithm from the part of teeth was created. The identification performance is 98% for the part of teeth excluded the right-molar and back teeth, 95% for the part of teeth excluded the front teeth and 80% for the part of teeth excluded the left-molar and back-teeth. [ABSTRACT FROM AUTHOR]

Application and research of ear recognition technology is a new subject in the field of biometrics recognition. Earlier research showed that human ear is one of the representative human biometrics with uniqueness and stability. Feasibility and characteristics of ear recognition was discussed and recent advances in 2D and 3D domain was presented. Furthermore, a proposal for future research topics was given, such as ear database generation, ear detection, ear occluding problem and multimodal biometrics with face etc. [ABSTRACT FROM AUTHOR]

Interest in automated biometrics continues to increase, but has little consideration of time. This paper deals with a problem of recognition by gait when time-dependent and time-invariant covariates are added. We have shown previously how recognition rates fall significantly for data captured over lengthy time intervals. We suggest predictive models of changes in gait due both to time and now to time-invariant covariates. A considerable improvement in recognition capability is demonstrated, with potential generic biometric application. [ABSTRACT FROM AUTHOR]

Gait is a biometric feature and gait recognition has become a challenging problem in computer vision. New wavelet velocity moments have been developed to describe and recognize gait. Wavelet moments are translation, scale and rotation invariant. Wavelet analysis has the trait of multi-resolution analysis, which strengthens the analysis ability to image subtle feature. According with the psychological studies, reflective symmetry features are introduced to help recognition. Combination of wavelet velocity moments and reflective symmetry not only has the characteristic of wavelet moments, but also reflects the person's walking habit of symmetry. Experiments on two databases show the proposed combined features of wavelet velocity moments and reflective symmetry are efficient to describe gait. [ABSTRACT FROM AUTHOR]

Handwriting recognition is a traditional and natural approach for personal authentication. Compared to signature verification, text-independent writer identification has gained more attention for its advantage of denying imposters in recent years. Dynamic features and static features of the handwriting are usually adopted for writer identification separately. For text-independent writer identification, by using a single classifier with the dynamic or the static feature, the accuracy is low, and many characters are required (more than 150 characters on average). In this paper, we developed a writer identification method to combine the matching results of two classifiers which employs the static feature (texture) and dynamic features individually. Sum-Rule, Common Weighted Sum-Rule and User-specific Sum-Rule are applied as the fusion strategy. Especially, we gave an improvement for the user-specific Sum-Rule algorithm by using an error-score. Experiments were conducted on the NLPR handwriting database involving 55 persons. The results show that the combination methods can improve the identification accuracy and reduce the number of characters required. [ABSTRACT FROM AUTHOR]

Function-based methods for on-line signature verification are studied. These methods are classified into local and regional depending on the features used for matching. One representative method of each class is selected from the literature. The selected local and regional methods are based on Dynamic Time Warping and Hidden Markov Models, respectively. Some improvements are presented for the local method aimed at strengthening the performance against skilled forgeries. The two methods are compared following the protocol defined in the Signature Verification Competition 2004. Fusion results are also provided demonstrating the complementary nature of these two approaches. [ABSTRACT FROM AUTHOR]

Several works related to information fusion for signature verification have been presented. However, few works have focused on sensor fusion and sensor interoperability. In this paper, these two topics are evaluated for signature verification using two different commercial Tablet PCs. An enrolment strategy using signatures from the two Tablet PCs is also proposed. Authentication performance experiments are reported by using a database with over 3000 signatures. [ABSTRACT FROM AUTHOR]

In this paper, Individual Dimension Gaussian Mixture Model (IDGMM) is proposed for speaker identification. As to the training-purpose feature vector series of a certain register, its joint probability distribution function (PDF) of is modeled by the product of the PDF of each dimension (marginal PDF), the scalar-based Gaussian Mixture Model (GMM) serving as the marginal PDF. For a good discriminative capability, the decorrelation by Schmidt orthogonalization and the Mixture Component Number (MCN) decision are adopted during the train. A close-set text-independent speaker identification experiment is also given. The simulation result shows that the IDGMM accelerates the training process remarkably and maintains the discriminative capability in testing process. [ABSTRACT FROM AUTHOR]

In this paper, a class of GMM-based discriminative kernels is proposed for speaker identification. We map an utterance vector into a matrix by finding the sequence of components, which have the maximum likelihood in the GMM for the all frame vectors. And the weights matrix was used, which were got by the GMM's parameters. Then the SVMs are used for classification. A one-versus-rest fashion is used for the c class problem. Results on YOHO in text-independent case show that the method can improve the performance greatly compared with the basic GMM. [ABSTRACT FROM AUTHOR]

In this paper, we propose an iris detection method to determine iris existence. The method extracts 4 types of features, i.e., contrast feature, symmetric feature, isotropy feature and disconnectedness feature. Adaboost is adopted to combine these features to build a strong cascaded classifier. Experiments show that the performance of the method is promising in terms of high speed, accuracy and device independence. [ABSTRACT FROM AUTHOR]

In this paper, we present an iris recognition system considering counterfeit attacks. The proposed system takes multi-spectral images instead of one infrared iris image. The energy of the multi-spectral images is checked and the authentication is failed if the amount of the energy is not in the proper range. Then the images are normalized and merged into a grayscale image by using a gradient-based image fusion algorithm. In the fusion process, the images considered to be from a counterfeited iris are merged into a poor-quality image which successively generates poor matching score. We show that the proposed scheme successfully maintains the performance of real iris images preventing the counterfeit attacks with experimental results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new feature extraction method based on independent component analysis (ICA) for iris recognition, which is known as the most reliable biometric system. We extract iris features using a bank of filters which are selected from the ICA basis functions. The ICA basis functions themselves are sufficient to be used as filter kernels for extracting iris features because they are estimated by training iris signals. Using techniques of the ICA estimation, we generate many kinds of candidates ICA filters. To select the ICA filters for extracting salient features efficiently, we introduce the requirements of the ICA filter. Each ICA filter has a different filter size and a good discrimination power to identify iris pattern. Also, the correlation between bandwidths of the ICA filters is minimized. Experimental results show that the EER of proposed ICA filter bank is better than those of existing methods in both the Yonsei iris database and CASIA iris database. [ABSTRACT FROM AUTHOR]

Reliable detection of irises is a necessary precondition for any iris-based biometric identification system. It is also important in measuring the movement of eyes in applications such as alertness detection and eye-guided user interfaces. This paper introduces a novel iterative algorithm for the accurate and fast localization of irises in eye images. [ABSTRACT FROM AUTHOR]

Automatic fingerprint classification is an effective means to increase the matching speed of an Automatic Fingerprint Identification System with a large-scale fingerprint database. In this paper, an automatic fingerprint classification method is proposed to classify the fingerprint image into one of five classes: Arch, Left loop, Right Loop, Whorl and Tented Arch. First, the information of core points, which is detected with a two-stage method, is applied to determine the reference point in fingerprint image. Then three different features based on statistical properties of small image blocks, which are likely to degrade with image quality deterioration, are calculated from the region of interest and form a 300-dimension feature vector. The feature vector is inputted into a three-layer Back Propagation Network (BPN) classifier and a 5-dimension vector is outputted, each dimension of which corresponds to one of 5 fingerprint classes. Finally, the fingerprints are classified with integrate analysis of the BPN classifier output and singular point information. The accuracy of 93.23% with no rejection is achieved on NIST-4 database and experimental results show that the proposed method is feasible and reliable for fingerprint classification. [ABSTRACT FROM AUTHOR]

Fingerprint enhancement is a crucial step in automatic fingerprint recognition system, undiscriminating repeated filtering easily lead to false structure in low-quality regions of a fingerprint image. This paper presents a new adaptive enhancement algorithm that can automatically adjust the parameters of filters and the time of filtering according to the quality factors in different regions. In order to improve filtering efficiency, a template bank of 4-dimenstion scaleable array is also designed to quantize the filter and forms the basis of a new fused filter. Experimental results in eight low-quality images from FVC2004 data sets show that the proposed algorithm is higher 23.7% in Good Index (GI), and saves 54.06% time consumptions than traditional Gabor-based methods. Since the eight images are extremely bad, a little improvement is very meaningful. [ABSTRACT FROM AUTHOR]

Unlike traditional minutiae-based fingerprint matching approach, which establishes the minutiae correspondence between two fingerprints, in this paper a novel ridge-based fingerprint matching algorithm, which establishes the exact ridge correspondence between two fingerprints is proposed. The ridge correspondence is called ‘exact' because correspondences between points on ridges are also established. The matching score is computed as the ratio of the length of matched ridges to that of all ridges. Experimental results on FVC2002 databases show that the ridge matching approach performs comparably to minutiae-based one, and an obvious improvement on matching performance can be obtained by combining the two matchers. [ABSTRACT FROM AUTHOR]

It is a difficult and challenge task to extract the accurate orientation field for the low quality fingerprints. This paper proposed a robust orientation estimation with feedback algorithm to get the accurate fingerprint orientation. First, the fingerprint image is segmented into recoverable and unrecoverable regions. The following orientation field estimation and orientation correction algorithms were only processed in the recoverable regions. Second, the coarse orientation image is estimated from the input fingerprint image using the gradient-based approach. Then we computed the reliability of orientation from the gradient image. If the reliability of the estimated orientation is less than pre-specified threshold, the orientation will be corrected by the proposed mixed orientation model. The proposed algorithm has been evaluated on the databases of FVC2004. Experimental results confirm that the proposed algorithm is a reliable and effective algorithm for the extraction orientation field of the low quality fingerprints. [ABSTRACT FROM AUTHOR]

This paper introduces Transductive Support Vector Machine (TSVM) into fingerprint verification. An improved fingerprint matching approach using TSVM is presented. In the proposed approach, the traditional minutiae-based fingerprint matching task is transformed to a classification task using TSVM. The paper presents an analysis of why TSVM are well suited for fingerprint matching, especially for small training sets. The approach is supported by experiments on five test collections, including both international and domestic fingerprint verification competition databases. Experimental results show that our approach is insensitive to noise as well as with effective performance. [ABSTRACT FROM AUTHOR]

A set of local feature descriptors for fingerprints is proposed. Minutia points are detected in a novel way by complex filtering of the structure tensor, not only revealing their position but also their direction. Parabolic and linear symmetry descriptions are used to model and extract local features including ridge orientation and reliability, which can be reused in several stages of fingerprint processing. Experimental results on the proposed technique are presented. [ABSTRACT FROM AUTHOR]

Though authentication using biometric techniques has conveniences for people, security problems like the leakage of personal bio-information would be serious. Even if cancelable biometric is a good solution for the problems, only a few biometric authentication scheme with cancelable feature has been published. In this paper, we suggest a face authentication scheme with two security factors: password and face image. Using matching algorithm in the permuted domain, our scheme is designed to be cancelable in the sense that templates that is composed of permutation and weight vector can be changed freely. [ABSTRACT FROM AUTHOR]

We propose two new classifiers, one based on the classical Procrustes analysis and the other on the Moore-Penrose inverse in the context of face recognition. The Procrustes based classifier has recognition rates of 97.5%, 96.19%, 71.40% and 96.22% for the ORL, YALE, GIT and the FERET database respectively. The Moore-Penrose classifier has comparative recognition rates of 98%, 99.04%, 87.40% and 96.22% for the same databases. In addition to these classifiers, we also propose new parameters that are useful for comparing classifiers based on their discriminatory power and not just on their recognition rates. We also compare the performance of our classifiers with the baseline PCA and LDA techniques as well as the recently proposed discriminative common vectors technique for the above face databases. [ABSTRACT FROM AUTHOR]

In this paper, we propose an automatic method to set up a 3D morphable face model. The main idea is to combine the algorithm of Fast-AAM with Thin Plate Splines (TPS) for 3D data alignment. Our method is better than the algorithm of optical flow proposed in [3][5] because it avoids the local minima problem, and also it is better than the adaptive multi-resolution fitting algorithm [8][9] in 3D alignment because it is fully automatic and the latter needs to be manually initialized. Result shows that our method is practicable in speed and accuracy. [ABSTRACT FROM AUTHOR]

Though AdaBoost has been widely used for feature selection and classifier learning, many of the selected features, or weak classifiers, are redundant. By incorporating mutual information into AdaBoost, we propose an improved boosting algorithm in this paper. The proposed method fully examines the redundancy between candidate classifiers and selected classifiers. The classifiers thus selected are both accurate and non-redundant. Experimental results show that the strong classifier learned using the proposed algorithm achieves a lower training error rate than AdaBoost. The proposed algorithm has also been applied to select discriminative Gabor features for face recognition. Even with the simple correlation distance measure and 1-NN classifier, the selected Gabor features achieve quite high recognition accuracy on the FERET database, where both expression and illumination variance exists. When only 140 features are used, the selected features achieve as high as 95.5% accuracy, which is about 2.5% higher than that of features selected by AdaBoost. [ABSTRACT FROM AUTHOR]

This paper investigates the score normalization technique for enhancing the performance of face authentication. We firstly discuss the thresholding approach for face authentication and put forward the "score variation" problem. Then, two possible solutions, Subject Specific Threshold (SST) and Score Normalization (SN), are discussed. But SST is obviously impractical to many face authentication applications in which only a single example face image is available for each subject. Fortunately, we have theoretically shown that, in such cases, score normalization technique may approximately approach the SST by using a uniform threshold. Experiments on both the FERET and CAS-PEAL face database have shown the effectiveness of SN for different face authentication methods including Correlation, Eigenface, and Fisherface. [ABSTRACT FROM AUTHOR]

The problem of illumination makes the face recognition still an unsolved problem. A new normalization method was presented which is illumination invariant in face recognition. The histogram equalization was applied to improve the performances of the AT (Affine Transform) algorithm. A novel illumination normalization method was introduced. Using the information of the distribution of the histogram, the method makes the combination of the AT algorithm and the ICR (Illumination Compensation based on Multiple Regression Model) algorithm smoothly. Experiments reveals that our proposed algorithm is illumination invariant and achieves better preprocess result while the recognition rate has been evidently improved. Keywords: Illumination normalization; face recognition; illumination invariant; degeneration model [ABSTRACT FROM AUTHOR]

This paper proposes a new method for personal identity verification based the analysis of face images applying One Class Support Vector Machines. This is a recently introduced kernel method to build a unary classifier to be trained by using only positive examples, avoiding the sensible choice of the impostor set typical of standard binary Support Vector Machines. The features of this classifier and the application to face-based identity verification are described and an implementation presented. Several experiments have been performed on both standard and proprietary databases. The tests performed, also in comparison with a standard classifier built on Support Vector Machines, clearly show the potential of the proposed approach. [ABSTRACT FROM AUTHOR]

Active Shape Model (ASM) has been recognized as one of the typical methods for image understanding. Simply speaking, it iterates two steps: profile-based landmarks local searching, and statistics-based global shape modeling. We argue that the simple 1D profile matching may not localize landmarks accurately enough, and the unreliable localized landmarks will mislead the following shape matching. Considering these two problems, we propose to enhance ASM from two aspects: (1) in the landmarks local searching step, we introduce more efficient AdaBoost method to localize some salient landmarks instead of the relatively simple profile matching as in the traditional ASMs; (2) in the global shape modeling step, the confidences of the landmark localization are exploited to constrain the shape modeling and reconstruction procedure by not using those unreliably located landmarks to eliminate their negative effects. We experimentally show that the proposed strategies can impressively improve the accuracy of the traditional ASMs. [ABSTRACT FROM AUTHOR]

While features used for texture analysis have been successfully used in some biometric applications, only quite few works have considered them in facial image analysis. Texture-based region descriptors can be very useful in recognizing faces and facial expressions, detecting faces and different facial components, and in other face related tasks. This paper demonstrates this issue by considering the local binary pattern (LBP) as an example of texture-based approach and showing its efficiency in facial image analysis. [ABSTRACT FROM AUTHOR]