Palmprint Features Research Articles

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments

Stereo-based palmprint recognition in various 3D postures

In order to increase performance in palmprint recognition systems, various devices are normally used to restrict the movement of the hand. These can cause problems, especially for those users with physical disabilities. They also cause significant hygiene problems in multi-user systems. Recently, studies on palmprint recognition systems have progressed towards the development of unconstrained, contactless and unrestricted background techniques. The most common problem encountered in these studies is the alignment arising from the free movement of the hand. Despite 3D hand-acquisition devices which offer extra recognition features to overcome this problem, the applicability of these devices is low because of their increased cost. In this study, a stereo camera was proposed. Although due to matching problems, it is difficult to achieve precise, distinct feature extraction in the unrestricted 3D environment used for palmprint recognition, the orientation of the hand in 3D space can be determined by obtaining depth information. In this study, the depth information was extracted by using the binocular stereo approach. First, the orientation of the hand was estimated by fitting a surface model associated with the eigenvectors of the depth information. Pose correction was then accomplished by establishing a relationship between the orientation and the images. The pose correction greatly relieved the perspective distortion that usually occurs within the various poses of the hands. Next, the region of interest was determined by performing segmentation on the corrected images using the Active Appearance Model (AAM). The palmprint features were then extracted via Gabor-based Kernel Fisher Discriminant Analysis. In order to demonstrate the performance of the proposed approach, a new dataset was compiled from stereo images within various scenarios collected from 138 different individuals. As a result of these experimental studies, the EER values, especially on the images captured from different hand orientations in 3D, were reduced from around 14–0.75%. With the help of this suggested approach, the palmprint recognition system was transformed into a more portable form by removing the closed-box mechanisms and equipment restricting movement of the hand. This system can automatically perform pose estimation, hand segmentation and recognition processes without any special intervention.

Collaborative representation with HM-LBP features for palmprint recognition

A novel collaborative representation model with hierarchical multiscale local binary pattern (HM-LBP) for palmprint recognition is proposed in this paper. HM-LBP can retrieve useful information from non-uniform patterns and reduce the influence of gray scale, rotation and illumination. The HM-LBP feature of palmprint is extracted, and its dimension is reduced by principal component analysis. And then, a collaborative classification with HM-LBP is presented to fully exploit the discrimination information. The proposed algorithm is evaluated on the Hong Kong Polytechnic University database (v2) to test its feasibility and performance. The results show that the algorithm can achieve ideal recognition accuracy of 100% and the speediness is able to fit for the real-time palmprint recognition system.

Feature Level Fusion of Multispectral Palmprint

Palmprint identification is one of the evolving technologies for personal authentication. Multispectral palmprint over the years have been widely used for authentication and security. This paper investigates the use of hybrid of Gabor filter and information sets for multispectral palmprint feature extraction. The Gabor feature vector has high dimensionality and it increases the time complexity of the identification. To overcome this we have extracted the information set based features from the Gabor features. Till now the hybrid of Gabor filter and information set based features has not been implemented. The rigorous experimental results ascertain that these features outperform the state-of-art features for multispectral palmprint. After this feature level fusion is performed, this converts two or more feature vectors into one feature vector and thus increases accuracy. Then in classification process individual’s palmprint is compared with the enrolled user’s palmprint in database by using K-nearest neighbor classifier. The performance of K-nearest neighbor classifier is compared with the SVM classifier. Accuracy of SVM classifier is more than the K-nearest neighbor classifier.

Multi-spectral palmprint recognition based on oriented multiscale log-Gabor filters

Among several palmprint recognition methods proposed recently, coding-based approaches using multi-spectral palmprint images are attractive owing to their high recognition rates. Aiming to further improve the performance of these approaches, this paper presents a novel multi-spectral palmprint recognition approach based on oriented multiscale log-Gabor filters. The proposed method aims to enhance the recognition performances by proposing novel solutions at three stages of the recognition process. Inspired by the bitwise competitive coding, the feature extraction employs a multi-resolution log-Gabor filtering where the final feature map is composed of the winning codes of the lowest filters’ bank response. The matching process employs a bitwise Hamming distance and Kullback–Leibler divergence as novel metrics to enable an efficient capture of the intra- and inter-similarities between palmprint feature maps. Finally, the decision stage is carried pout using a fusion of the scores generated from different spectral bands to reduce overlapping. In addition, a fusion of the feature maps through two proposed novel feature fusion techniques to allow us to eliminate the inherent redundancy of the features of neighboring spectral bands is also proposed. The experimental results obtained using the multi-spectral palmprint database MS-PolyU have shown that the proposed method achieves high accuracy in mono-spectral and multi-spectral recognition performances for both verification and identification modes; and also outperforms the state-of-the-art methods.

BI-MODAL PALM PRINT AND KNUCKLE PRINT RECOGNITION SYSTEM

This paper proposed an innovative contact-less palm print and knuckle print recognition system. Palm print is referred to as line textures, which contains principal lines, wrinkles and ridges on the inner surface of the palm. On the other hand, knuckle print is denoted as the flexion lines on the inner skin of the knuckles of the fingers. These line patterns are unique and stable, and they offer abundance of useful information for personal recognition. We present a novel palm print and knuckle print tracking approach to automatically detect and capture these features from low resolution video stream. No constraint is imposed and the subject can place his/her hand naturally on top of the input sensor without touching any device. The palm print and knuckle print features are extracted using our proposed Wavelet Gabor Competitive Code and Ridget Transform methods. Several decision-level fusion rules are used to consolidate the scores output by the palm print and knuckle print experts. The fusion of these features yields promising result of EER=1.25% for verification rate.

Suspecting Less and Doing Better: New Insights on Palmprint Identification for Faster and More Accurate Matching

This paper introduces a generalized palmprint identification framework to unify several state-of-art 2D and 3D palmprint methods. Through this framework, we argue that the methods employing one-to-one matching strategy and binary representation for feature are more effective for palmprint identification. The analysis for the first argument is based on a statistical matching model and is supported by outperforming results on several publicly available 2D palmprpint databases. These two arguments are further evaluated for 3D palmprint matching and used to introduce a new method for encoding 3D palmprint feature. The proposed 3D feature is binary and more efficiently computed. It encodes the 3D shape of palmprint to either convex or concave. The experimental results on two publicly available, from contactless and contact-base 3D palmprint database of 177 and 200 subjects, respectively, outperform the state-of-the-art methods. This paper also provides our palmprint matching algorithm(s) in public domain, unlike the previous work in this area, which will help to further advance research efforts in this area.

A Palmprint Recognition Approach Based on Image Segmentation of Region of Interest

To carry out an effective recognition for palmprint, this paper presents an algorithm of image segmentation of region of interest (ROI), extracts the ROI of a palmprint image and studies the composing features of palmprint. This paper constructs a coordinate by making use of characteristic points in the palm geometric contour, improves the algorithm of ROI extraction and provides a positioning method of ROI. Moreover, this paper uses the wavelet transform to divide up ROI, extracts the energy feature of wavelet, gives an approach of matching and recognition to improve the correctness and efficiency of existing main recognition approaches, and compares it with existing main approaches of palmprint recognition by experiments. The experiment results show that the approach in this paper has the better recognition effect, the faster matching speed, and the higher recognition rate which is improved averagely by 2.69% than those of the main recognition approaches.

Half-orientation extraction of palmprint features

The points in palmprint images usually have two different dominant orientations.Half-orientations can precisely represent the orientation feature of the palmprint.Defining a bank of half-Gabor fi...

A Novel Dictionary Learning Model with PT-HLBP for Palmprint Recognition

A novel projective dictionary pair learning (PDPL) model with statistical local features for palmprint recognition is proposed. Pooling technique is used to enhance the invariance of hierarchical local binary pattern (PT-HLBP) for palmprint feature extraction. PDPL is employed to learn an analysis dictionary and a synthesis dictionary which are utilized for image discrimination and representation. The proposed algorithm has been tested by the Hong Kong Polytechnic University (PolyU) database (v2) and ideal recognition accuracy can be achieved. Experimental results indicate that the algorithm not only greatly reduces the time complexity in training and testing phase, but also exhibits good robustness for image rotation and corrosion.

Competitive region orientation code for palmprint verification and identification

Orientation features of the palmprint have been widely investigated in coding-based palmprint-recognition methods. Conventional orientation-based coding methods usually used discrete filters to extract the orientation feature of palmprint. However, in real operations, the orientations of the filter usually are not consistent with the lines of the palmprint. We thus propose a competitive region orientation-based coding method. Furthermore, an effective weighted balance scheme is proposed to improve the accuracy of the extracted region orientation. Compared with conventional methods, the region orientation of the palmprint extracted using the proposed method can precisely and robustly describe the orientation feature of the palmprint. Extensive experiments on the baseline PolyU and multispectral palmprint databases are performed and the results show that the proposed method achieves a promising performance in comparison to conventional state-of-the-art orientation-based coding methods in both palmprint verification and identification.

Dual-source discrimination power analysis for multi-instance contactless palmprint recognition

Due to the benefits of palmprint recognition and the advantages of biometric fusion systems, it is necessary to study multi-source palmprint fusion systems. Unfortunately, the research on multi-instance palmprint feature fusion is absent until now. In this paper, we extract the features of left and right palmprints with two-dimensional discrete cosine transform (2DDCT) to constitute a dual-source space. Normalization is utilized in dual-source space to avoid the disturbance caused by the coefficients with large absolute values. Thus complicated pre-masking is needless and arbitrary removing of discriminative coefficients is avoided. Since more discriminative coefficients can be preserved and retrieved with discrimination power analysis (DPA) from dual-source space, the accuracy performance is improved. The experiments performed on contactless palmprint database confirm that dual-source DPA, which is designed for multi-instance palmprint feature fusion recognition, outperforms single-source DPA.

Half-orientation extraction of palmprint features

Orientation features of the palmprint are usually used in palmprint recognition methods. Conventional orientation based methods are always based on an assumption that the line in a palmprint is straight and possesses only a single dominant orientation. However, a large number of “lines” in a palmprint are curves. The point in these curves usually has two dominant orientations. Moreover, it can be seen that there are numerous cross wrinkles in a palmprint. The cross point of any two cross wrinkles obviously has two different dominant orientations. In this paper, we proposed a simple and effective double half-orientation based method for feature extraction and recognition of the palmprint. In the method, a bank of “half-Gabor” filters are defined for the half-orientation extraction of a palmprint. Compared with the single dominant orientation, the double half-orientations can more precisely characterize the global orientation feature of a palmprint. Extensive experiments are carried out on three different kinds of palmprint databases and the results show that the proposed method achieves a promising performance in both palmprint verification and identification and outperforms other orientation feature based methods.

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

Palmprint is usually captured with a touchless device. Due to the changes of angle and position of the palm in a capturing process, as well as the defocus of device, it is inevitable to have some distortions in translation, rotation, and image blurriness, which would degrade the performance of a palmprint recognition system. In order to effectively solve the low recognition problem due to image blurriness, we propose a robust palmprint recognition system in this paper by using the fast Vese–Osher decomposition model to process the blurred palmprint images. First, a Gaussian defocus degradation model (GDDM) is proposed to characterize the image blurriness, and we can observe from this model that there are some stable features in the palmprint images with different scale of blurriness. Second, the structure layer and texture layer of blurred palmprint images are obtained by using the fast Vese–Osher decomposition model, and the structure layer is proved to be more stable and robust than the texture layer for palmprint recognition. Consequently, a novel algorithm based on the weighted histogram of oriented gradient for locally selected pattern (WHOG-LSP) is proposed and it is used to extract some robust features from the structure layer of blurred palmprint images. These extracted features can be used to address low performance issues associated with translation and rotation in palmprint recognition. Finally, the normalized correlation coefficient (NCC) is used to measure the similarity of palmprint features for the proposed recognition system. Extensive experiments on the PolyU palmprint database and the blurred PolyU palmprint database validate the effectiveness and real-time applicability of the proposed recognition system. In addition, an experiment is carried out in the IIT Delhi Touchless Palmprint Database for verifying the robustness of the proposed method.

Deformed Palmprint Matching Based on Stable Regions.

Palmprint recognition (PR) is an effective technology for personal recognition. A main problem, which deteriorates the performance of PR, is the deformations of palmprint images. This problem becomes more severe on contactless occasions, in which images are acquired without any guiding mechanisms, and hence critically limits the applications of PR. To solve the deformation problems, in this paper, a model for non-linearly deformed palmprint matching is derived by approximating non-linear deformed palmprint images with piecewise-linear deformed stable regions. Based on this model, a novel approach for deformed palmprint matching, named key point-based block growing (KPBG), is proposed. In KPBG, an iterative M-estimator sample consensus algorithm based on scale invariant feature transform features is devised to compute piecewise-linear transformations to approximate the non-linear deformations of palmprints, and then, the stable regions complying with the linear transformations are decided using a block growing algorithm. Palmprint feature extraction and matching are performed over these stable regions to compute matching scores for decision. Experiments on several public palmprint databases show that the proposed models and the KPBG approach can effectively solve the deformation problem in palmprint verification and outperform the state-of-the-art methods.

Local line directional pattern for palmprint recognition

Local binary patterns (LBP) are one of the most important image representations. However, LBPs have not been as successful as other methods in palmprint recognition. Originally, the LBP descriptor methods construct feature vectors in the image intensity space, using pixel intensity differences to encode a local representation of the image. Recently, similar feature descriptors have been proposed which operate in the gradient space instead of the image intensity space, such as local directional patterns (LDP) and local directional number patterns (LDN). In this paper, we propose a new feature input space and define an LBP-like descriptor that operates in the local line-geometry space, thus proposing a new image descriptor, local line directional patterns (LLDP). Moreover, the purpose of this work is to show that different implementations of LLDP descriptors perform competitively in palmprint recognition. We evaluate variations to LLDPs, e.g., the modified finite radon transform (MFRAT) and the real part of Gabor filters are exploited to extract robust directional palmprint features. As is well-known, palm lines are the essential features of a palmprint. We are able to show that the proposed LLDP descriptors are suitable for robust palmprint recognition. Finally, we present a thorough performance comparison among different LBP-like and LLDP image descriptors. Based on experimental results, the proposed feature encoding of LLDPs using directional indexing can achieve better recognition performance than that of bit strings in the Gabor-based implementation of LLDPs. We used four databases for performance comparisons: the Hong Kong Polytechnic University Palmprint Database II, the blue band of the Hong Kong Polytechnic University Multispectral Palmprint Database, the Cross-Sensor palmprint database, and the IIT Delhi touchless palmprint database. Overall, LLDP descriptors achieve a performance that is competitive or better than other LBP descriptors.

Feature Level Fusion the Performance of Multimodal Biometric Systems

paper proposed the use of multimodal feature-level fusion to prove the improvement performance of multimodal authentication. Different algorithm used for features extraction, LG for extracting FKP features, LPQ for iris and Palmprint features extraction, and PCA for extracting face features. Results brought to light that the multimodal authentication process gained higher performance than single modality. The biometric performance using feature-level fusions under Z-score, Tanh, Median, and Min-Max normalization has been demonstrated in this paper.

Double-orientation code and nonlinear matching scheme for palmprint recognition

Many palmprint authentication approaches have been proposed in recent years. Among them, the orientation based coding approach, in which the dominant orientation features of palmprints are extracted and encoded into bitwise codes, is one of the most promising approaches. The distance between codes created from two palmprint images is calculated in the matching stage. Reliable orientation feature extraction and efficient matching are the two most crucial problems in orientation based coding approaches. However, conventional coding based approaches usually extract only one dominant orientation feature by adopting filters with discrete orientations, which is sensitive to the noise and rotation. This paper proposed a novel double-orientation code (DOC) scheme to represent the orientation feature of palmprint and designed an effective nonlinear angular matching score to evaluate the similarity between the DOC. Extensive experiments performed on three types of palmprint databases demonstrate that the proposed approach has excellent performance in comparison with previously proposed state-of-the-art approaches.

Blur and occlusion invariant palmprint recognition with block-wise local phase quantization histogram

A method for palmprint verification and identification, which is robust to blurred and occluded palmprints is proposed. An algorithm is proposed for region of interest (ROI) extraction that aligns each palmprint ROI on the same position. First-level decomposition of the ROI using a Haar wavelet gives an approximation ROI (AROI) and reduces computational overhead. Local phase quantization (LPQ) based on quantization of phase information by local Fourier transform is applied on AROI to obtain blur invariant palmprint features. The LPQ image is divided into M×M nonoverlapping blocks, and histograms of these blocks are considered as features. The entropy of each block is used to identify it as occluded or nonoccluded. The average chi-square distance between corresponding nonoccluded blocks is calculated for measuring similarity. Experiments performed on PolyU 2D, CASIA, IITD, and IIITDMJ palmprint databases show that the proposed system is independent of acquisition device. Moreover, to justify the robustness of the proposed system, experiments are performed with variable amounts of blur and occlusion present in test palmprints. The performance of the proposed system is compared with several local feature-based palmprint recognition techniques on normal as well as occluded palmprints.

A Novel Gaussian Measure Curvelet Based Feature Segmentation and Extraction for Palmprint Images

Objectives: An effective feature extraction and segmentation model is employed for palm print images to improve accuracy, computation efficiency and robustness of palm print features. Methods/Analysis: The novel Gaussian Measure Curve let based Feature Segmentation and Extraction (GMC-SE) method is introduced for removal of unwanted execution time by using Edge Based Tangent (EBT) model. In addition, to improve the computation efficiency of features being segmented, competent Gaussian measure is obtained by integrating both local and global palm print features. Findings: Experiment is conducted using Poly U 2D palm print database to measure the effectiveness of the proposed work in terms of execution time ratio, computation efficiency, feature extraction accuracy and robustness in palm print recognition. The proposed scheme GMC-SE method is compared against the existing Fine Ridge Structure Dictionary (FRSD) and Personal Identification using Left and Right Palm Print images (PI-LRPP). As a result, the GMC-SE method improves the computation efficiency by 12% compared to existing FRSD model. Conclusion/Application: An effective feature extraction and segmentation are analyzed for palm print images and experimental results are compared. GMC-SE method for palm print images handled different images in an efficient manner compared to existing works.