3-D palmprint modeling for biometric verification

This paper presents a work about palm print recognition using fuzzy entropy. A number of schemes have been proposed to combine the fuzzy set theory and its application to the entropy concept for modelling a palm print recognition system. The measure of uncertainty is adopted as a measure of information. Hence, the measures of fuzziness are known as fuzzy information measures. The measure of information is used to measure the uncertainty. Hence, fuzzy information measures are used to measure the fuzziness. The measure of a quantity of fuzzy information obtained from a fuzzy set or fuzzy system is known as fuzzy entropy. Since no probabilistic concept is needed to define fuzzy entropy, classical Shannon entropy is quite different from it. Further, Shannon entropy contains the randomness uncertainty (probabilistic) whereas fuzzy entropy contains vagueness and ambiguity uncertainties. The concept of membership function is used to define fuzzy entropy. The properties of fuzzy entropy have become popular and are being used very frequently to define any new fuzzy entropy. The fuzzy entropy function used in this paper is defined using the new random membership function μ. Previous works in palm print recognition use standard methods for feature extraction. By using standard techniques, the recognition rate was not as expected. Incorporating fuzzy concept to derive the features of palm print is the motivation for this work. The proposed work uses fuzzy membership function to extract the maximum information out of palm print as it contains lot of uncertainties. The fuzzy membership function developed in the proposed work is applied in the fuzzy entropy function to generate the new entropy information features which when tested with the standard classifier using support vector machine gives the encouraging result in terms of recognition rate. The proposed work was done using COEP (College of Engineering, Pune) database. It can be noted that palm print recognition is an important modality in biometrics for security purposes, as its use in practical life cannot be avoided. The palm print image contains lot of undefined and vague information and the best method to take out the maximum information out of the palm print image is the use of fuzzy logic concepts. This feature also motivated us for the proposed work.

This paper presents an algorithm to extract the region of interest (ROI) from the palm print image of the Hong Kong PolyU large-scale palm print database (version 2). Competitive coding method is used for feature extraction. Coding based methods are among the most promising palm print recognition methods because of their small feature size, fast matching speed, and high verification accuracy. Competitive Coding Scheme (CCS), first convolves the palm print image from real part of six Gabor filters with different orientations and then encodes the dominant orientation into its bit wise representation. Palm print image is decomposed into two levels using discrete wavelet transform. Approximation details give compress and denoised image of the original image. The competitive scheme is applied in the approximation details of decomposed image for feature extraction. KNN Classifier is used for palm print verification. In this experiment, Genuine Acceptance Rate is calculated for four different wavelet filters (Db1, Db4, Sym4, and Coif4) with different values of K (number of nearest neighbors).

Abstract: Palm print recognition techniques have made extensive and successful use of palm print direction patterns. In order to obtain authentic line responses in the palm print image, the majority of current direction-based techniques uses pre-defined filters, which necessitate extensive prior knowledge and typically overlooks crucial direction information. Furthermore, some noise-influenced line replies will reduce the accuracy of recognition. Another challenge for enhancing recognition performance is figuring out how to extract the discriminative elements that will make the palm print easier to distinguish. We suggest using this work to understand comprehensive and discriminative direction patterns in order to address these issues. Using the palm print picture, we first extract the complete and salient local direction patterns, which include a salient convolution difference feature (SCDF) and a complete local direction feature (CLDF). Two learning models are then suggested in order to obtain the underlying structure for the SCDFs in the training samples and to learn discriminative and sparse directions from CLDF, respectively. Finally, the full and discriminative direction feature for palm print recognition is formed by concatenating the projected CLDF and the projected SCDF. The efficiency of the suggested strategy is amply demonstrated by experimental findings on three noisy datasets and seven palm print databases.

The major issues that involve in identifying palm print are the search for the templates in the palm print database that best matches with the test sample from input. Here the fundamental to be solved are to select similar features of palm that are needs to be matched. The different feature of palm print that are able to discriminate them from each other must show a huge divergence between different users samples and few divergence between same user samples. For verification process principal lines and datum points are successfully used as an important palm print features, but some other features that are associated with a palm print are delta point features, geometry features, minutiae features and wrinkle features. By using the existing techniques, we propose a distinct scheme to facilitate the dynamic selection of palm print pattern to match it by combining various global and local features of a palm print in a hierarchical way. Our palm print matching system will operate in two steps, enrollment of user and verification of user. In first step that is enrollment; several palm print samples are obtained by a user to store them as templates in the system. Palm print scanner is used to capture the samples which then pass through preprocessing and feature extraction to create the templates which then stored in predefined palm print database. In second and final step, the palm print scanner is used to capture the fresh palm print sample of user. Then the captured palm print sample again passes through preprocessing and feature extraction. These extracted features of a user are compared with existing templates in the database to verify the identity of the user. In this paper, we are proposing a hybrid approach for palm print recognition using a combination of three different approaches of Image processing.

Robust palmprint recognition based on the fast variation Vese–Osher model

Palm print scanning is a widespread method for biometric identity detection which has some advantages over other methods including its simplicity and relatively lower cost. In this study, a novel methods for biometric verification and identification by contactless palm scanning technique is proposed. In the study, Ripplet-I Transform (R-IT) which is a generalized form of Curvelet Transform (CuT), have been used in addition to multi-resolution transforms which were previously used in the literature as palm print verification and identification methods such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Contourlet Transform (CoT). In addition, Principal Component Analysis (PCA) and Local Binary Pattern (LBP) have been utilized to increase the algorithm diversity. In order to investigate the effect of classification methods on the study results and the processing times, Artificial Neural Network (ANN), Euclidean Distance (ED) and Support Vector Machine (SVM) have been used separately for matching in the verification part of study. The performance of Convolutional Neural Network (CNN) as a classifier has also been examined. Verification and identification algorithms proposed in the study have been tested using palm print images of Hong Kong Polytechnic University Contact-free 3D/2D Hand Images Database (Version 1.0). The studies, that were carried out under two main sections yielded interesting results. At the end of the study, AUC (Area Under the ROC Curve) values ranging from 0.550 (Equal Error Rate (EER)= 0.4594) to 0.9875 (EER= 0.0336) were obtained for palm print verification. The highest AUC value without using LBP was obtained as 0.9563 (EER= 0.1096) using R-IT/CuT+DCT+CNN. Study results were showed that CNN is more successful than other classifiers without using LBP. It also has pointed out that the R-IT/CuT provides better results than the DWT and CoT. Using LBP in algorithms has increased success for ED, SVM and ANN. However, it has reduced overall for CNN. The highest AUC value (0.9875 and EER= 0.0336) was provided by the LBP+DWT+ED algorithm for palm print verification. The highest Identification Rate (IR) was achieved by using the LBP+CoT+ED algorithm with 84.444% for for palm print identification.

The paper presents performance comparison of palm print identification techniques based on fractional coefficients of transformed palm print image using three different transforms like Sine, Walsh and Slant. In transform domain, the energy of image gets accumulated towards high frequency region; this characteristic of image transforms is exploited here to reduce the feature vector size of palm print images by selecting these high frequency coefficients in transformed palm print images. Three image transforms and 12 ways of taking fractional coefficients result into total 36 palm print identification methods. A database of 2350 palm print images is used as a test bed for performance comparison of the proposed palm print identification methods with help of false acceptance rate (FAR) and genuine acceptance rate (GAR). The experimental results in Sine and Walsh transform have shown performance improvement in palm print identification using fractional coefficients of transformed images. In all Sine transform at fractional coefficients of 0.78% gives best performance as indicated by higher GAR value. Thus the task of speeding up palm print identification with better performance is achieved to make it more suitable for real time applications.KeywordsPalm PrintBiometricSine TransformWalsh TransformSlant Transform

Biometrics recognition takes advantage of feature extraction and pattern recognition to analyze the physical and behavioral characteristics of biological individuals to achieve the purpose of individual identification. As a typical biometric technology, palm print and palm vein have the characteristics of high recognition rate, stable features, easy location and good image quality, which have attracted the attention of researchers. This paper designs and develops a multispectral palm print and palm vein acquisition platform, which can quickly acquire palm spectrum and palm vein multispectral images with seven different wavelengths. We propose a multispectral palm print palmar vein recognition framework, and feature-level image fusion is performed after extracting features of palm print palmar vein images at different wavelengths. Through the multispectral palm print palm vein image fusion experiment, a more feasible multispectral palm print and palm vein image fusion scheme is proposed. Based on the results of image fusion, we further propose an improved convolutional neural network (CNN) for model training to achieve identity recognition based on multispectral palm print palm vein images. Finally, the effects of different CNN network structures and learning rates on the recognition results were analyzed and compared experimentally.

With the increasing demand of highly accurate and robust authentication system, palm print has widely been used in personal identification. Multispectral palm print has been proposed to get more distinguishing feature information and improve resistance to spoof. The vivid texture information of palm print presenting at different resolutions offers abundant prospects in personal recognition. So this paper describes a method to authenticate individuals based on multi-scale and multi-resolution palm print recognition. First, we extract palm print feature based on NSCT (nonsubsampled Contour transform), and store the feature using a hash table. Then the scores generated from each set of palm print image under red, green, blue and NIR spectrum, are combined using score level fusion method. This method use SUM and MAX operators. Comparatively low values of equal error rate and high recognition rate have been obtained for all fusion techniques. Multispectral palm print verification results on the ROI image libraries of PolyU demonstrate the effectiveness and accuracy of the proposed method.

Compared with the Palm line structure features, extraction and description of palm print texture features are easier. But, with the increase in the number of palmprint samples, these features are not powerful enough. In order to solve the problem, the paper proposes a new approach to enhance the distinguishing capability of texture features for palm print recognition. It uses classical results on Riemannian geometry to obtain the information of palm lines and construct direction fields of palm lines. The direction fields become a part of the textures of the palmprint image to enhance the distinguishing capability of texture features. Finally, the dual-tree complex wavelet transform-based local binary pattern weighted histogram method (DT -CWT based LBPWH) is used to extract enhanced texture features. The experimental results validate the effectiveness of the method.

Touch-less palm print biometrics: Novel design and implementation

Palmprint has proved to be one of the most unique and stable biometric characteristics. Almost all the current palmprint recognition techniques capture the 2-D image of the palm surface and use it for feature extraction and matching. Although 2-D palmprint recognition can achieve high accuracy, the 2-D palmprint images can be counterfeited easily and much 3-D depth information is lost in the imaging process. This paper explores a 3-D palmprint recognition approach by exploiting the 3-D structural information of the palm surface. The structured light imaging is used to acquire the 3-D palmprint data, from which several types of unique features, including mean curvature image, Gaussian curvature image, and surface type, are extracted. A fast feature matching and score-level fusion strategy are proposed for palmprint matching and classification. With the established 3-D palmprint database, a series of verification and identification experiments is conducted to evaluate the proposed method. The results demonstrate that 3-D palmprint technique has high recognition performance. Although its recognition rate is a little lower than 2-D palmprint recognition, 3-D palmprint recognition has higher anticounterfeiting capability and is more robust to illumination variations and serious scrabbling in the palm surface. Meanwhile, by fusing the 2-D and 3-D palmprint information, much higher recognition rate can be achieved.

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

This paper presents an innovative contactless palm print and palm vein recognition system. We design a hand sensor that could capture the palm print and palm vein image using low-resolution web camera. Both the visible and infrared images can be captured at the same time, and we do not need specialized infrared sensor to image the vein pattern. The design of the device is simple and low-cost. The subject can be shielded completely from the complication of undergoing two separate acquiring processes. We allow subjects to position their hands freely above the sensor and they can move their hands during the acquisition process. In order to obtain clear image of the palm vascular pattern, we propose a novel image enhancement technique called local-ridge-enhancement (LRE). The proposed method removes illumination error while keeping good contrast between the print/vein pattern and the background image. Besides, we introduce a simple yet robust directional coding technique to encode the palm print and palm vein features in bit string representation. The bit string representation offers speedy template matching and enables more effective template storage and retrieval. The scores output by the palm print and palm vein experts are fused using Support Vector Machine. The fusion of these features yields promising result for practical implementation.

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.