Palmprint processes a number of unique features for reliable personal recognition. However, different types of palmprint images contain different dominant features. Instead, only some features of the palmprint are visible in a palmprint image, whereas the other features may not be notable. For example, the low-resolution palmprint image has visible principal lines and wrinkles. By contrast, the high-resolution palmprint image contains clear ridge patterns and minutiae points. In addition, the three dimensional (3-D) palmprint image possesses curvatures of the palmprint surface. So far, there is no work to summarize the feature extraction of different types of palmprint images. In this paper, we have an aim to completely study the feature extraction and recognition of palmprint. We propose to use a unified framework to classify palmprint images into four categories: 1) the contact-based; 2) contactless; 3) high-resolution; and 4) 3-D palmprint images. Then, we analyze the motivations and theories of the representative extraction and matching methods for different types of palmprint images. Finally, we compare and test the state-of-the-art methods via the widely used palmprint databases, and point out some potential directions for future research.

Feature Extraction Methods for Palmprint Recognition: A Survey and Evaluation

Various palmprint recognition methods have been proposed based on orientation features of palmprints. Among them, the competitive code method using the dominant orientation of palmprint images achieves promising performance in palmprint recognition. In this paper, we propose a discriminative and robust competitive code based method, which uses a more accurate dominant orientation representation of palmprint images for palmprint authentication. Moreover, we propose to weight the orientation information of a neighbor area to improve the precision and stability of the discriminative and robust dominant orientation code. Experiments performed on three types of palmprint databases and a noisy dataset validate the effectiveness of the proposed method.

Discriminative and Robust Competitive Code for Palmprint Recognition

We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly processes 3D point cloud scenes with large motions in a coarse-to-fine fashion. Flow computed at the coarse level is upsampled and warped to a finer level, enabling the algorithm to accommodate for large motion without a prohibitive search space. We introduce novel cost volume, upsampling, and warping layers to efficiently handle 3D point cloud data. Unlike traditional cost volumes that require exhaustively computing all the cost values on a high-dimensional grid, our point-based formulation discretizes the cost volume onto input 3D points, and a PointConv operation efficiently computes convolutions on the cost volume. Experiment results on FlyingThings3D and KITTI outperform the state-of-the-art by a large margin. We further explore novel self-supervised losses to train our model and achieve comparable results to state-of-the-art trained with supervised loss. Without any fine-tuning, our method also shows great generalization ability on the KITTI Scene Flow 2015 dataset, outperforming all previous methods. The code is released at https://github.com/DylanWusee/PointPWC.

PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation

Half-orientation extraction of palmprint features

With the rapid development of information storage and networking technologies, quintillion bytes of data are generated every day from social networks, business transactions, sensors, and many other domains. The increasing data volumes impose significant challenges to traditional data analysis tools in storing, processing, and analyzing these extremely large-scale data. For decades, hashing has been one of the most effective tools commonly used to compress data for fast access and analysis, as well as information integrity verification. Hashing techniques have also evolved from simple randomization approaches to advanced adaptive methods considering locality, structure, label information, and data security, for effective hashing. This survey reviews and categorizes existing hashing techniques as a taxonomy, in order to provide a comprehensive view of mainstream hashing techniques for different types of data and applications. The taxonomy also studies the uniqueness of each method and therefore can serve as technique references in understanding the niche of different hashing mechanisms for future development.

Hashing Techniques: A Survey and Taxonomy

In this work, we propose a combinative strategy based on regression and clustering for solving point set matching problems under a Bayesian framework, in which the regression estimates the transformation from the model to the scene and the clustering establishes the correspondence between two point sets. The point set matching model is illustrated by a hierarchical directed graph, and the matching uncertainties are approximated by a coarse-to-fine variational inference algorithm. Furthermore, two Gaussian mixtures are proposed for the estimation of heteroscedastic noise and spurious outliers, and an isotropic or anisotropic covariance can be imposed on each mixture in terms of the transformed model points. The experimental results show that the proposed approach achieves comparable performance to state-of-the-art matching or registration algorithms in terms of both robustness and accuracy.

Probabilistic Model for Robust Affine and Non-Rigid Point Set Matching

In this paper, we present two hashing based techniques for fast palmprint identification. We first propose three properties required by a hash function and then introduce our first fast identification method based on orientation pattern (OP) hashing. We give the definition of OP and demonstrate that it meets all the requirements of the hash function, and thus, would be appropriate for hashing based fast palmprint identification. We then introduce the second fast identification method based on principal orientation pattern (POP) hashing. Because the POPs are constructed using more stable orientation features, POP hashing can find the target template more quickly thus causing earlier termination of the identification process. We evaluate our methods on the Hong Kong PolyU large-scale database (9667 palms) and the CASIA palmprint database (600 palms) plus a synthetic database (100 000 palms). Experimental results show that, on the Hong Kong PolyU large-scale database, the speedups of OP hashing and POP hashing over brute-force search are 16.93 and 19.91, respectively, and the identification accuracy is slightly higher. While on the CASIA database plus the synthetic database, the speedups of OP hashing and POP hashing are 8.03 and 15.67, respectively, and the identification accuracy almost remains the same. Results also show that, in terms of accuracy, our methods are comparable to several state-of-the-art palmprint identification approaches, while in terms of speed, our methods are much faster.

Hashing Based Fast Palmprint Identification for Large-Scale Databases

An equidistant fish-eye camera can be calibrated using a single image of two sets of parallel lines in the scene, which is very convenient for practical applications. The core of the calibration pro- cedure is the fitting of center collinear circles that intersect at two van- ishing points. A fast and accurate fitting method that can be used for equidistant fish-eye camera calibration is presented. First, the fitting problem is formulated as a nonlinear least square problem and solved using Levenberg-Marquardt optimization. The objective func- tion is also derived, as well as the Jacobian required by the Levenberg-Marquardt algorithm. It is also demonstrated how to deter- mine the initial estimations. Experimental results on synthetic data have demonstrated the superiority of our method to two existing approaches in terms of speed and accuracy. Results on real data have also demonstrated its effectiveness for fish-eye camera calibra- tion. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publica- tion, including its DOI. (DOI: 10.1117/1.JEI.22.1.013004)

https://www.spiedigitallibrary.org/journals/journal-of-electronic-imaging/volume-22/issue-1/013004/Direct-fitting-of-center-collinear-circles-for-equidistant-fish-eye/10.1117/1.JEI.22.1.013004.pdf

Direct fitting of center collinear circles for equidistant fish-eye camera calibration

In this work, we propose a variational approximation approach in combination with isotropic Gaussian mixtures with regard to each individual transformed points for point set matching problems. A variational inference algorithm is formulated to update the posteriors of the random variables in sequence until a local optimum is reached. The probabilistic framework explicitly accounts for matching uncertainty and is thus less prone to local optima. Furthermore, the Gaussian mixtures with anisotropic covariance are also proposed for the modeling of spurious points instead of the one uniform distribution. The experimental results show that the combination of variational approximation with mixture model provides our algorithm with comparable performance of accuracy and robustness to other registration algorithms in the presence of outliers.

Probabilistic point set matching with Gaussian mixture model

A typical palmprint identification system identifies a query palmprint by matching it with the templates in the database and comparing the result with the pre-defined similarity threshold. When applied on large-scale databases, it is often necessary to speed up this process. Previous study shows that POP hashing is very effective for fast and accurate palmprint identification. As state-of-the-art approach, its speedup is around 15 to 20 on databases with 10,000 to 100,000 templates. In this paper, we investigate the POP hashing algorithm in more depth, and propose to accelerate it by improved pre-processing approach, i.e. building the hash table using more consistent orientation features. Experimental results on several real and synthetic databases show that the proposed method can accelerate POP hashing up to 50%∼85%, with negligible accuracy loss. Meanwhile, the space requirement of the hash table is reduced by a factor of 2, making it more appealing for practical applications.

Bin Li

Papers

Probabilistic Model for Robust Affine and Non-Rigid Point Set Matching

Hashing Based Fast Palmprint Identification for Large-Scale Databases

Direct fitting of center collinear circles for equidistant fish-eye camera calibration

Probabilistic point set matching with Gaussian mixture model

Accelerated POP hashing for fast palmprint identification on large-scale databases