Palmprint Recognition Algorithm Research Articles

Deep palmprint recognition algorithm based on self-supervised learning and uncertainty loss

Deep palmprint recognition algorithm based on self-supervised learning and uncertainty loss

Few-shot palmprint recognition based on similarity metric hashing network

Palmprint recognition is one of the effective biometric technologies due to the advantages of convenience and safety. Recently, many deep learning-based methods are utilized in palmprint recognition and achieve satisfactory results. However, most of the existing learning methods are driven by the abundant labeled data. When the training data are insufficient, their performance drop sharply. In this work, we propose a novel and effective end-to-end algorithm for few-shot palmprint recognition, called Similarity Metric Hashing Network (SMHNet). SMHNet is designed to extract the features of palmprint images on both the structural and pixel levels. Specifically, an embedded structural similarity (SSIM) index block is constructed behind the last convolution layer to measure the structural similarity between query samples and support ones. A novel SSIM loss is designed with distance loss to train the entire model from scratch. Furthermore, a hashing block is added after the last fully connected (FC) layer to encode the features into hashing codes, which is convenient for large-scale feature storage and retrieval. Extensive experiments are conducted on three benchmark palmprint databases, and the results demonstrate that our model can achieve competitive accuracy compared with several state-of-the-art models.

One-shot cross-dataset palmprint recognition via adversarial domain adaptation

Deep learning-based palmprint recognition algorithms have obtained promising performance. However, the previous methods require a large amount of labeled samples, which are difficult to obtain. In this paper, a novel cross-dataset palmprint recognition method is proposed using as low as one labelled sample per subject in the target palmprint dataset based on adversarial domain adaptation. Two different palmprint datasets are adopted as source dataset and target dataset. The training samples from two datasets are grouped into four categories. MobileFaceNets-based deep hashing network (DHN) is introduced to extract discriminative features, which can improve the efficiencies of feature extracting and matching. To align the features in two datasets, a typical adversarial discriminator is augmented to distinguish between the four different categories. With adversarial learning, the target network is becoming adaptive to the unlabeled target palmprint images. Extensive experiments on the benchmarks including constrained and unconstrained palmprint databases demonstrate that our method can outperform the baseline models on cross-dataset palmprint verification and identification.

Keypoint Selection Algorithm for Palmprint Recognition with SURF

In this article we employ SURF features for palmprint characterization and recognition. We developed an original method (we named it FIKEN) for computing a fixed number of SURF keypoints. The classification process is based on the nearest neighbor ratio procedure and a distance measure that uses the locations of the matched keypoints. The experiments were performed on three well-known palmprint databases, IITD, CASIA and GPDS. The obtained experimental results show that the proposed FIKEN method gave very good recognition results that outperformed state-of-the-art results for these collections of palmprint images.

Few-Shot Learning for Palmprint Recognition via Meta-Siamese Network

Palmprint is one of the discriminant biometric modalities of humans. Recently, deep learning-based palmprint recognition algorithms have improved the accuracy and robustness of recognition results to a new level. Most of them require a large amount of labeled training samples to guarantee satisfactory performance. However, getting enough labeled data is difficult due to time consumption and privacy issues. Therefore, in this article, a novel meta-Siamese network (MSN) is proposed to exploit few-shot learning for small-sample palmprint recognition. During each episode-based training iteration, a few images are selected as sample and query sets to simulate the support and testing sets in the test set. Specifically, the model is trained episodically with a flexible framework to learn both the feature embedding and deep similarity metric function. In addition, two distance-based losses are introduced to assist the optimization. After training, the model can learn the ability to get similarity scores between two images for few-shot testing. Adequate experiments conducted on several constrained and unconstrained benchmark palmprint databases show that MSN can obtain competitive improvements compared with baseline methods, where the best accuracy can be up to 100%.

Towards Cross-Dataset Palmprint Recognition Via Joint Pixel and Feature Alignment.

Deep learning-based palmprint recognition algorithms have shown great potential. Most of them are mainly focused on identifying samples from the same dataset. However, they may be not suitable for a more convenient case that the images for training and test are from different datasets, such as collected by embedded terminals and smartphones. Therefore, we propose a novel Joint Pixel and Feature Alignment (JPFA) framework for such cross-dataset palmprint recognition scenarios. Two-stage alignment is applied to obtain adaptive features in source and target datasets. 1) Deep style transfer model is adopted to convert source images into fake images to reduce the dataset gaps and perform data augmentation on pixel level. 2) A new deep domain adaptation model is proposed to extract adaptive features by aligning the dataset-specific distributions of target-source and target-fake pairs on feature level. Adequate experiments are conducted on several benchmarks including constrained and unconstrained palmprint databases. The results demonstrate that our JPFA outperforms other models to achieve the state-of-the-arts. Compared with baseline, the accuracy of cross-dataset identification is improved by up to 28.10% and the Equal Error Rate (EER) of cross-dataset verification is reduced by up to 4.69%. To make our results reproducible, the codes are publicly available at http://gr.xjtu.edu.cn/web/bell/resource.

Deep Distillation Hashing for Unconstrained Palmprint Recognition

Deep palmprint recognition has become an emerging issue with great potential for personal authentication on handheld and wearable consumer devices. Previous studies of palmprint recognition are mainly based on constrained data sets collected by dedicated devices in controlled environments, which has to reduce the flexibility and convenience. In addition, general deep palmprint recognition algorithms are often too heavy to meet the real-time requirements of embedded system. In this article, a new palmprint benchmark is established, which consists of more than 20 000 images collected by five brands of smartphones in an unconstrained manner. Each image has been manually labeled with 14 key points for the region of interest (ROI) extraction. Furthermore, a novel deep distillation hashing (DDH) algorithm is proposed as a benchmark for efficient deep palmprint recognition. Palmprint images are converted to binary codes to improve the efficiency of feature matching. Derived from knowledge distillation, new distillation loss functions are constructed to compress the deep model to further improve the efficiency of feature extraction on the light network. Comprehensive experiments are conducted on both constrained and unconstrained palmprint databases. Using DDH, the accuracy of palmprint identification can be increased by up to 11.37%, and the equal error rate (EER) of palmprint verification can be reduced by up to 3.11%. The results indicate the potential of our database, and DDH can outperform other baselines to achieve the state-of-the-art performance. The collected data set is publicly available at http://gr.xjtu.edu.cn/web/bell/resource .

Toward Palmprint Recognition Methodology Based Machine Learning Techniques

The presented paper proposes an algorithm for palmprint recognition using seven different machine learning algorithms. First of all, we have proposed a region of interest (ROI) extraction methodology which is a two key points technique. Secondly, we have performed some image enhancement techniques such as edge detection and morphological operations in order to make the ROI image more suitable for the Hough transform. In addition, we have applied the Hough transform in order to extract all the possible principle lines on the ROI images. We have extracted the most salient morphological features of those lines; slope and length. Furthermore, we have applied the invariant moments algorithm in order to produce 7 appropriate hues of interest. Finally, after performing a complete hybrid feature vectors, we have applied different machine learning algorithms in order to recognize palmprints effectively. Recognition accuracy have been tested by calculating precision, sensitivity, specificity, accuracy, dice, Jaccard coefficients, correlation coefficients, and training time. Seven different supervised machine learning algorithms have been implemented and utilized. The effect of forming the proposed hybrid feature vectors between Hough transform and Invariant moment have been utilized and tested. Experimental results show that the feed forward neural network with back propagation has achieved about 99.99% recognition accuracy among all tested machine learning techniques.

An Improved Recognition Approach for Noisy Multispectral Palmprint by Robust L2 Sparse Representation with a Tensor-Based Extreme Learning Machine.

For the past decades, recognition technologies of multispectral palmprint have attracted more and more attention due to their abundant spatial and spectral characteristics compared with the single spectral case. Enlightened by this, an innovative robust L2 sparse representation with tensor-based extreme learning machine (RL2SR-TELM) algorithm is put forward by using an adaptive image level fusion strategy to accomplish the multispectral palmprint recognition. Firstly, we construct a robust L2 sparse representation (RL2SR) optimization model to calculate the linear representation coefficients. To suppress the affection caused by noise contamination, we introduce a logistic function into RL2SR model to evaluate the representation residual. Secondly, we propose a novel weighted sparse and collaborative concentration index (WSCCI) to calculate the fusion weight adaptively. Finally, we put forward a TELM approach to carry out the classification task. It can deal with the high dimension data directly and reserve the image spatial information well. Extensive experiments are implemented on the benchmark multispectral palmprint database provided by PolyU. The experiment results validate that our RL2SR-TELM algorithm overmatches a number of state-of-the-art multispectral palmprint recognition algorithms both when the images are noise-free and contaminated by different noises.

Retraction notice to Palmprint recognition algorithm with horizontally expanded blanket dimension NEUCOM 127, 15 March 2014, Pages 152–160

Retraction notice to Palmprint recognition algorithm with horizontally expanded blanket dimension NEUCOM 127, 15 March 2014, Pages 152–160

Palmprint Recognition Based on Complete Direction Representation.

Direction information serves as one of the most important features for palmprint recognition. In the past decade, many effective direction representation (DR)-based methods have been proposed and achieved promising recognition performance. However, due to an incomplete understanding for DR, these methods only extract DR in one direction level and one scale. Hence, they did not fully utilize all potentials of DR. In addition, most researchers only focused on the DR extraction in spatial coding domain, and rarely considered the methods in frequency domain. In this paper, we propose a general framework for DR-based method named complete DR (CDR), which reveals DR by a comprehensive and complete way. Different from traditional methods, CDR emphasizes the use of direction information with strategies of multi-scale, multi-direction level, multi-region, as well as feature selection or learning. This way, CDR subsumes previous methods as special cases. Moreover, thanks to its new insight, CDR can guide the design of new DR-based methods toward better performance. Motived this way, we propose a novel palmprint recognition algorithm in frequency domain. First, we extract CDR using multi-scale modified finite radon transformation. Then, an effective correlation filter, namely, band-limited phase-only correlation, is explored for pattern matching. To remove feature redundancy, the sequential forward selection method is used to select a small number of CDR images. Finally, the matching scores obtained from different selected features are integrated using score-level-fusion. Experiments demonstrate that our method can achieve better recognition accuracy than the other state-of-the-art methods. More importantly, it has fast matching speed, making it quite suitable for the large-scale identification applications.

Selective Algorithm Outline (SAO); An Alternative Approach for Fusing Different Palm-Print Recognition Algorithms

In recent years, there has been renewed interest in integrating holistic-based and feature-based palm-print recognition approaches in a cascade way for improving the recognition performance. They u...

A novel palmprint recognition algorithm based on Curvelet invariant features of the surface

A novel palmprint recognition algorithm based on Curvelet invariant features of the surface

3D Palmprint Recognition Using Dempster-Shafer Fusion Theory

This paper proposed a novel 3D palmprint recognition algorithm by combining 3D palmprint features using D-S fusion theory. Firstly, the structured light imaging is used to acquire the 3D palmprint data. Secondly, two types of unique features, including mean curvature feature and Gaussian curvature feature, are extracted. Thirdly, the belief function of the mean curvature recognition and the Gaussian curvature recognition was assigned, respectively. Fourthly, the fusion belief function from the proposed method was determined by the Dempster-shafer (D-S) fusion theory. Finally, palmprint recognition was accomplished according to the classification criteria. A 3D palmprint database with 1000 range images from 100 individuals was established, on which extensive experiments were performed. The results show that the proposed method 3D palmprint recognition is much more robust to illumination variations and condition changes of palmprint than MCR and GCR. Meanwhile, by fusing mean curvature and Gaussian curvature feature, the experimental results are promising (the average equal error rate of 0.404%). In the future, imaging technique needs further improvement for a better recognition performance.

Research on Applied Technology of Palmprint Recognition Based on Fisher Linear Discriminant and Improved PCA Algorithm

The information applied technology of palmprint recognition is a biometric technology, it’s based on the effective information on the palm (such as: palmprint) to identifies people. The palmprint is unique and characteristic, these are the identification of critical conditions. The feature extraction of palmprint image is a prerequisite for recognition, feature extraction algorithm depends on the quality of the recognition rate and efficiency. This paper presents a method of palmprint recognition algorithm based on Fisher linear discriminant analysis and improved PCA algorithm. The experimental results show that, the recognition rate is improved.

RETRACTED: Palmprint recognition algorithm with horizontally expanded blanket dimension

As an emerging biometric technology, palmprint recognition has been extensively researched due to its easy collection, user friendliness, high verification accuracy and reliability. Blanket dimension is a commonly used fractal dimension and has the multi-resolution characteristics with which the image texture information can be better extracted. In this work, palmprint recognition with blanket dimension and its expansions was investigated. The efficiencies of horizontally and vertically expanded blanket dimensions for extracting the directional feature of palmprint were compared, and a palmprint recognition algorithm based on horizontally expanded blanket dimension (HEBD) was proposed according to the comparison results. Furthermore, a multi-scale HEBD (MHEBD) algorithm for palmprint recognition was also presented, and the MHEBD was demonstrated to be more effective than the single-scale HEBD for feature extraction. The algorithm was evaluated on Hong Kong Polytechnic University (PolyU) database (v2) and CASIA database. The experimental results indicate that the multi-scale HEBD can extract the palmprint features effectively and efficiently with a high recognition rate and less processing time.

Palmprint Recognition Algorithm Based on Principal Component Analysis

Palmprint Recognition Algorithm Based on Principal Component Analysis

A histogram-based dominant wavelet domain feature selection algorithm for palm-print recognition

A feature extraction algorithm for palm-print recognition based on two dimensional discrete wavelet transform is proposed in this paper, which efficiently exploits the local spatial variations in a palm-print image. The palm-image is segmented into several spatial modules and a palm-print recognition scheme is developed, which extracts histogram-based dominant wavelet features from each of these local modules. The effect of modularization in terms of the entropy content of the palm-print images has been analyzed. The selection of dominant features for the purpose of recognition not only drastically reduces the feature dimension but also captures precisely the detail variations within the palm-print image. It is shown that, the modularization of the palm-print image enhances the discriminating capabilities of the proposed features and thereby results in high within-class compactness and between-class separability of the extracted features. Different types of Daubechies wavelets (in terms of use of number of vanishing moments, i.e., db1–db10) have been utilized for the purpose of feature extraction and the effect upon the recognition performance has been also investigated. In order to further reduce the feature dimension, a principal component analysis is performed. It is found from our extensive experimentations on different palm-print databases that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.

On-line fast palmprint identification based on adaptive lifting wavelet scheme

The recent on-line palmprint recognition algorithms are time-consuming, and not suitable for being implemented with hardware. This paper describes a novel on-line fast palmprint identification approach. In order to reduce the computational cost of extracting palmprint features from a palmprint image and make it easy to be implemented with hardware, we construct an adaptive lifting wavelet scheme to decompose a palmprint image into several subbands, and then the pulse-coupled neural network is employed to decompose each subband into a series of binary images. The entropies of these binary images are calculated and regarded as features. Then, in the classification step, a support vector machine-based classifier is utilized. Experimental results show that the proposed approach yields a better performance in terms of the correct classification percentages compared with the recent on-line palmprint recognition algorithms. It is also shown that the proposed approach yields observably low computational cost and can be easily implemented with hardware.

Palm Print Recognition using Zernike Moments

ABSTRACT This paper proposes an automated system for recognizing palmprints for biometric identification of individuals. Complex Zernike moments are constructed using a set of complex polynomials which form a complete orthogonal basis set defined on the unit disc. Palmprint images are projected onto the basis set resulting in a set of complex signals. The magnitude of the complex value is computed and a scalar value is derived from it by computing the mean of the vector elements. Classification is done by subtracting the test samples from the mean of the training set. The data set consists of 80 images divided into 4 classes. Accuracy obtained is comparable to the best results reported in literature General Terms Pattern Recognition, Computer Vision Keywords Zernike moment, Palmprint recognition, Texture classification 1. INTRODUCTION Biometrics refers to automatic recognition of individuals based on their physiological and behavioral characteristics, which are nowadays used extensively for personal identification worldwide. Different physical traits like face, iris, fingerprint, palmprint, retina etc. fall under the perview of biometrics. Palmprint recognition involves identifying an individual by the principal lines, wrinkles, ridges on the surface of the palm. The basis for using palmprints lies in the fact that no two individuals have exactly the same palmprint pattern, moreover palmprints remain more or less stable throughout the lifetime and are easily obtainable using standard imaging techniques. Challenges in palmprint recognition are related to building a reliable data model from randomly oriented irregular lines that enable high amount of accuracies in security based systems and applications. This paper presents an efficient algorithm for palm print recognition by utilizing complex Zernike moments. The organization of the paper is as follows: section 2 provides an overview of the related works, section 3 outlines the proposed approach, section 4 details the experimentations done and results obtained, section 5 analyses the current work vis-a-vis contemporary works, section 6 brings up the overall conclusions and future scopes.