Palmprints are emerging as a new entity in multi-modal biometrics for human identification and verification. Multispectral palmprint images captured in the visible and infrared spectrum not only contain the wrinkles and ridge structure of a palm, but also the underlying pattern of veins; making them a highly discriminating biometric identifier. In this paper, we propose a feature encoding scheme for robust and highly accurate representation and matching of multispectral palmprints. To facilitate compact storage of the feature, we design a binary hash table structure that allows for efficient matching in large databases. Comprehensive experiments for both identification and verification scenarios are performed on two public datasets -- one captured with a contact-based sensor (PolyU dataset), and the other with a contact-free sensor (CASIA dataset). Recognition results in various experimental setups show that the proposed method consistently outperforms existing state-of-the-art methods. Error rates achieved by our method (0.003 on PolyU and 0.2 on CASIA) are the lowest reported in literature on both dataset and clearly indicate the viability of palmprint as a reliable and promising biometric. All source codes are publicly available.
Palmprint is a reliable and accurate trait in the biometric family. To improve the accuracy of the existing palmprint systems, multispectral imaging is used for palmprint recognition. Although image level fusion and matching score level fusion were widely used for multispectral palmprint recognition, feature level fusion was paid less attention. This paper proposes a novel method for multispectral palmprint recognition by the feature level fusion technique. Experimental results on a large public multispectral database show that the proposed method could get better results than image level fusion, and it could get comparable results with matching score level fusion with less cost of storage. Multispectral imaging has been effectively used in the fields of computer vision and human verification to analyze information captured from several bands of the electromagnetic spectrum recently. In this paper, a novel Gabor-based palm print identification algorithm on multispectral images is proposed. Various features are extracted from convolving the Gabor kernels with each image sample. Logistic regression method is used to fuse obtained ranks of each spectral band authentication. The proposed algorithm is evaluated on PolyU database which contains palm print images from 400 individuals from each band. The obtained results show robustness of our algorithm in comparison with other works that presented in the literature. As a unique and reliable biometric characteristic, palmprint verification has achieved a great success. However, palmprint alone may not be able to meet the increasing demand of highly accurate and robust biometric systems. Recently, palmvein, which refers to the palm feature under near-infrared spectrum, has been attracting much research interest. Since palmprint and palmvein can be captured simultaneously by using specially designed devices, the joint use of palmprint and palmvein features can effectively increase the accuracy, robustness and anti-spoof capability of palm based biometric techniques. This paper presents an online personal verification system by fusing palmprint and palmvein inforA fast palmprint and palmvein recognition systemA fast palmprint and palmvein recognition system quality can vary much, a dynamic fusion scheme which is adaptive to image quality is developed. To increase the anti-spoof capability of the system, a liveness detection method based on the image property is proposed. A comprehensive database of palmprint-palmvein images was established to verify the proposed system, and the experimental results demonstrated that since palmprint and palmvein contain complementary information, much higher accuracy could be achieved by fusing them than using only one of them. In addition, the whole verification procedure can be completed in 1.2s, which implies that the system can work in real time. This paper presents an intra-modal fusion environment to integrate multiple raw palm images at low level. Fusion of palmprint instances is performed by wavelet transform and decomposition. To capture the palm characteristics, fused image is convolved with Gabor wavelet transform. The Gabor wavelet feature representation reflects very high dimensional space. To reduce the high dimensionality, ant colony optimization algorithm is applied to select relevant, distinctive and reduced feature set from Gabor responses. Finally, the reduced set of features is trained with support vector machines and accomplished user recognition tasks. For evaluation, CASIA multispectral palmprint database is used. The experimental results reveal that the system is found to be robust and encouraging while variations of classifiers are used. Also a comparative study is presented of the proposed system with a well-known method. Palmprint is widely used in personal identification for an accurate and robust recognition. To improve the existing palmprint systems, the proposed system, which is the first on-line multispectral palmprint recognition system ever designed before, uses multispectral capture device to sense images under different illumination, including red, green, blue and infrared. We adopt competitive coding scheme as matching algorithm, which performs well in on-line palmprint recognition. Wavelet-based image fusion method is used as data-level fusion strategy in our scheme. Fused verifications show better effort on motion blurred source images than single channel. Experimental results of fusion images are also useful references for future work on multispectral palmprint recognition. In this paper, we propose a novel palmprint verification approach based on principal lines. In feature extraction stage, the modified finite Radon transform is proposed, which can extract principal lines effectively and efficiently even in the case that the palmprint images contain many long and strong wrinkles. In matching stage, a matching algorithm based on pixel-to-area comparison is devised to calculate the similarity between two palmprints, which has shown good robustness for slight rotations and translations of palmprints. The experimental results for the verification on Hong Kong Polytechnic University Palmprint Database show that the discriminability of principal lines is also strong. This paper presents a novel technique to identify palmprints of individuals for various purposes including security, access control, forensic applications, identification, etc. Palmprints, known to be more robust as biometrics are being increasingly used in these areas. In this paper the identification of the palmprint of an individual has been done using a transform domain technique where a new transform using the Kronecker product of the existing transforms (DCT and Walsh) is developed and applied to multi-spectral palmprint images. Energy compaction technique in transform domain is applied to reduce the size of feature vector. The properties of both DCT and Walsh transforms are incorporated in the new transform which gives better results than when both the transforms are used individually. The GAR values have been computed for different values of energy considered. The maximum value of GAR obtained is 98.53 for an energy threshold of 99.99 on palmprints under blue illumination. The FAR is found to be 4 . We describe the design and development of a prototype whole-hand imaging system. The sensor is based on multispectral technology that is able to provide hand shape, fingerprints and palmprint modalities of a user's hand by a single user interaction with the sensor. A clear advantage of our system over other unimodal sensors for these modalities include: (i) faster acquisition time, (ii) better quality images, and (iii) ability to provide spoof detection. Initial results on a medium-size database show good recognition performance based on individual modalities as well as after fusing multiple fingers and fusing finger and palm. The prototype is being refined in order to improve performance even further. This correspondence paper studies face recognition by using hyperspectral imagery in the visible light bands. The spectral measurements over the visible spectrum have different discriminatory information for the task of face identification, and it is found that the absorption bands related to hemoglobin are more discriminative than the other bands. Therefore, feature band selection based on the physical absorption characteristics of face skin is performed, and two feature band subsets are selected. Then, three methods are proposed for hyperspectral face recognition, including whole band (2D)2PCA, single band (2D)2PCA with decision level fusion, and band subset fusion-based (2D)2PCA. A simple yet efficient decision level fusion strategy is also proposed for the latter two methods. To testify the proposed techniques, a hyperspectral face database was established which contains 25 subjects and has 33 bands over the visible light spectrum (0.4-0.72 μm). The experimental results demonstrated that hyperspectral face recognition with the selected feature bands outperforms that by using a single band, using the whole bands, or, interestingly, using the conventional RGB color bands. In this paper, we propose to improve the verification performance of a contract-free palmprint recognition system by means of feature- level image registration and pixel-level fusion of multi-spectral palm images. Our method involves image acquisition via a dedicated device under contact-free and multi-spectral environment, preprocessing to locate region of interest (ROI) from each individual hand images, feature-level registration to align ROIs from different spectral images in one sequence and fusion to combine images from multiple spectra. The advantages of the proposed method include better hygiene and higher verification performance. Given a database composed of images from 330 hands, two out of four state of the art fusion strategies offer significant performance gain and the best equal error rate (EER) is 0.5 . Palm print is a unique and reliable biometric characteristic with high usability. Many palm print recognition algorithms and systems have been successfully developed in the past decades. Most of the previous works use the white light sources for illumination. Recently, it has been attracting much research attention on developing new biometric systems with both high accuracy and high anti-spoof capability. Multispectral palm print imaging and recognition can be a potential solution to such systems because it can acquire more discriminative information for personal identity recognition. One crucial step in developing such systems is how to determine the minimal number of spectral bands and select the most representative bands to build the multispectral imaging system. This paper presents preliminary studies on feature band selection by analyzing hyper spectral palm print data (420nm 1100nm). Our experiments showed that 2 spectral bands at 700nm and 960nm could provide most discriminate information of palm print. This finding could be used as the guidance for designing multispectral palm print systems in the future. Personal identification problem has been a major field of research in recent years. Biometrics-based technologies that exploit fingerprints, iris, face, voice and palmprints, have been in the center of attention to solve this problem. Palmprints can be used instead of fingerprints that have been of the earliest of these biometrics technologies. A palm is covered with the same skin as the fingertips but has a larger surface, giving us more information than the fingertips. The major features of the palm are palm-lines, including principal lines, wrinkles and ridges. Using these lines is one of the most popular approaches towards solving the palmprint recognition problem. Another robust feature is the wavelet energy of palms. In this paper we used a hybrid feature which combines both of these features. Moreover, multispectral analysis is applied to improve the performance of the system. At the end, minimum distance classifier is used to match test images with one of the training samples. The proposed algorithm has been tested on a well-known multispectral palmprint dataset and achieved an average accuracy of 98.8 . Abstract-Palmprint has been widely used in personal recognition. To improve the performance of the existing palmprint recognition system, multispectral palmprint recognition system has been proposed and designed. This paper presents a method of representing the multispectral palmprint images by quaternion and extracting features using the quaternion principal components analysis (QPCA) to achieve better performance in recognition. A data acquisition device is employed to capture the palmprint images under Red, Green, Blue and near-infrared (NIR) illuminations in less than 1s. QPCA is used to extract features of multispectral palmprint images. The dissimilarity between two palmprint images is measured by the Euclidean distance. The experiment shows that a higher recognition rate can be achieved when we use QPCA. Given 3000 testing samples from 500 palms, the best GAR is 98.13 . Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7 spl mu m-1.0 spl mu m). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression. Hand biometrics, including fingerprint, palmprint, hand geometry and hand vein pattern, have obtained extensive attention in recent years. Physiologically, skin is a complex multi-layered tissue consisting of various types of components. Optical research suggests that different components appear when the skin is illuminated with light sources of different wavelengths. This motivates us to extend the capability of camera by integrating information from multispectral palm images to a composite representation that conveys richer and denser pattern for recognition. Besides, usability and security of the whole system might be boosted at the same time. In this paper, comparative study of several pixel level multispectral palm image fusion approaches is conducted and several well-established criteria are utilized as objective fusion quality evaluation measure. Among others, Curvelet transform is found to perform best in preserving discriminative patterns from multispectral palm images. Ensuring the security of individuals is becoming an increasingly important problem in a variety of applications. Biometrics technology that relies on the physical and or behavior human characteristics is capable of providing the necessary security over the standard forms of identification. Palmprint recognition is a relatively new one. Almost all the current palmprint-recognition systems are mainly based on image captured under visible light. However, multispectral and hyperspectral imaging have been recently used to improve the performance of palmprint identification. In this paper, the MultiSpectral Palmprint (MSP) and HyperSpectral Palmprint (HSP) are integrated in order to construct an efficient multimodal biometric system. The observation vector is based on Principal Components Analysis (PCA). Subsequently, HiddenMarkov Model (HMM) is used for modeling this vector. The proposed scheme is tested and evaluated using 350 users. Our experimental results show the effectiveness and reliability of the proposed system, which brings high identification accuracy rate. This paper explores the possibility of using multispectral iris information to enhance the recognition performance of an iris biometric system. Commercial iris recognition systems typically sense the iridal reflection pertaining to the near-infrared (IR) range of the electromagnetic spectrum. This work examines the iris information represented in the visible and IR portion of the spectrum. It is hypothesized that, based on the color of the eye, different components of the iris are highlighted at multiple wavelengths. To this end, an acquisition procedure for obtaining co-registered multispectral iris images associated with the IR, Red, Green and Blue wavelengths of the electromagnetic spectrum, is first discussed. The components of the iris that are revealed in multiple spectral channels wavelengths based on the color of the eye are studied. An adaptive histogram equalization scheme is invoked to enhance the iris structure. The performance of iris recognition across multiple wavelengths is next evaluated. Experiments indicate the potential of using multispectral information to enhance the performance of iris recognition systems. Palmprint is a unique and reliable biometric characteristic with high usability. With the increasing demand of highly accurate and robust palmprint authentication system, multispectral imaging has been employed to acquire more discriminative information and increase the antispoof capability of palmprint. This paper presents an online multispectral palmprint system that could meet the requirement of real-time application. A data acquisition device is designed to capture the palmprint images under Blue, Green, Red, and near-infrared (NIR) illuminations in less than 1 s. A large multispectral palmprint database is then established to investigate the recognition performance of each spectral band. Our experimental results show that the red channel achieves the best result, whereas the Blue and Green channels have comparable performance but are slightly inferior to the NIR channel. After analyzing the extracted features from different bands, we propose a score level fusion scheme to integrate the multispectral information. The palmprint verification experiments demonstrated the superiority of multispectral fusion to each single spectrum, which results in both higher verification accuracy and antispoofing capability. This paper presents an approach for the personal authentication using rank-level fusion of multispectral palmprints, instead of using multiple biometric modalities and multiple matchers. The rank level fusion involving the non linear combination of hyperbolic tangent functions gives the best recognition rate for the Rank 1 obtained from two types of features, viz., sigmoid and fuzzy. The results of using rank level fusion on the publicly available multispectral palmprint database show the significant improvement in the recognition rate as compared to the individual spectral bands. Recognition rate of 99.4 from sigmoid features and that of 99.2 from fuzzy features based on Rank 1 is the outcome of the hyperbolic tangent nonlinearity. Palmprint recognition has been investigated over 10 years. During this period, many different problems related to palmprint recognition have been addressed. This paper provides an overview of current palmprint research, describing in particular capture devices, preprocessing, verification algorithms, palmprint-related fusion, algorithms especially designed for real-time palmprint identification in large databases and measures for protecting palmprint systems and users' privacy. Finally, some suggestion is offered.