Efficient local representations for three-dimensional palmprint recognition

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.

2-D palmprint has been recognized as an effective biometric identifier in the past decade. Recently, 3-D palmprint recognition was proposed to further improve the performance of palmprint systems. This paper presents a simple yet efficient scheme for 3-D palmprint recognition. After calculating and enhancing the mean-curvature image of the 3-D palmprint data, we extract both line and orientation features from it. The two types of features are then fused at either score level or feature level for the final 3-D palmprint recognition. The experiments on The Hong Kong Polytechnic University 3-D palmprint database, which contains 8000 samples from 400 palms show that the proposed feature extraction and fusion methods lead to promising performance.

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

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.

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.

International audience

Palmprint is a very unique and distinctive biometric trait because of features such as a person's inimitable principal lines, wrinkles, delta points, and minutiae. These constitute the main reasons why palmprint verification is considered as one of the most reliable personal identification methods. However, a clear majority of the research on palm-prints are concentrated on 2-D palmprint images irrespective of the fact that the human palm is a 3D-surface. While 2-D palmprint recognition has proved to be efficient in terms of verification rate, it has some essential downsides. These restrictions can adversely affect the performance and robustness of the palmprint recognition system. One of the possible solutions to resolve the limitations associated with 2-D palm print authentication systems is (i) to use a 3-D scanning system and to produce high quality 3-D images with depth information; (ii) to map 3-D palm-print images into 2-D images which may support the usage of 3-D images with both biometric palmprint 2-D image databases and 2-D palmprint recognition tools. The bloom of 3-D technologies has made it easier to capture and store 3-D images. The problem of a direct mapping approach is that a large section of the palm is hard-pressed on the scanner surface during 2-D based acquisition. This paper proposes a novel technique to unravel/map 3-D palm images to its equivalent 2-D palm-print image. This image can be then used to perform efficient and accurate 2-D identification/ verification. Experimental results and discussions will also be presented.

With the widespread application of biometrics in identification systems, palmprint recognition technology, as an emerging biometric technology, has received more and more attention in recent years. Palmprint recognition mainly focuses on image acquisition, preprocessing, feature selection and image matching. Feature extraction and matching are usually the most essential processes in palmprint recognition, and most of the research is based on feature selection and image matching, and many researchers use rich knowledge in machine learning and computer vision to solve these problems. In this paper, we propose a deep multi-view representation learning based multi-spectral palmprint fusion method, which uses deep neural networks to extract feature representation of multi-spectral palmprint images for palmprint classification. In this manner, the unique features of different spectral palmprint images can be used to learn a view-invariant representation of each palmprint. By using view-invariant representation, we can get better palmprint recognition performance than single modality. Experiments are performed on PolyU palmprint data set to validate the effectiveness of the proposed method.

Local line directional pattern for palmprint recognition

Palmprint recognition provides a potential solution for noninvasive personal authentication due to its excellent contactless property and user-security, and it has attracted tremendous research interest in recent years. However, most existing methods focus on intraspectral palmprint recognition, which requires gallery and probe images to be captured under similar illumination, and thus significantly limit its practical applications in open environments with variant illuminations. In this study, we present a spectrum-invariant feature learning method for cross-spectral palmprint recognition to address the problem that gallery and probe samples are captured under different spectra. First, the blockwise direction-based ordinal measure vectors are formed to represent the intrinsic information of palmprint images. Then, a unified feature projection is jointly learned to map two different spectra of palmprint images into a common feature space, in which the different spectral features have enhanced discriminative power by enlarging their variances while the intraclass features learned from different spectral images are similar. The proposed method can be easily extended to seek the unified spectrum-invariant representation of multiple spectral palmprint images, making it feasible to perform palmprint recognition crossing one spectrum to multiple spectra. Experimental results on two multispectral palmprint image databases demonstrate the promising effectiveness of the proposed method on cross-spectral palmprint recognition.

Palmprint recognition (PPR) has recently garnered attention due to its robustness and accuracy. Many PPR methods rely on preprocessing the region of interest (ROI). However, the emergence of ROI attacks capable of generating synthetic ROI images poses a significant threat to PPR systems. Despite this, ROI attacks are less practical since PPR systems typically take hand images as input rather than just the ROI. Therefore, there is a pressing need for a method that specifically targets the system by composing hand images. The intuitive approach involves embedding an ROI into a hand image, a comparatively simpler process requiring less data than generating entirely synthetic images. However, embedding faces challenges, as the composited hand image must maintain a consistent color and texture. To overcome these challenges, we propose a training-free, end-to-end hand image composition method incorporating ROI harmonization and palm blending. The ROI harmonization process iteratively adjusts the ROI to seamlessly integrate with the hand using a modified style transfer method. Simultaneously, palm blending employs a pretrained inpainting model to composite a hand image with a continuous transition. Our results demonstrate that the proposed method achieves a high attack performance on the IITD and Tongji datasets, with the composited hand images exhibiting realistic visual quality.

Palmprint recognition, as a reliable personal identity check method, has been receiving increasing attention during recent years. According to previous work, local texture analysis supplies the most promising framework for palmprint image representation. In this paper, we propose a novel palmprint recognition method by combining statistical texture descriptions of local image regions and their spatial relations. In our method, for each image block, a spatial enhanced histogram of gradient directions is used to represent discriminative texture features. Furthermore, we measure similarity between two palmprint images using a simple graph matching scheme, making use of structural information. Experimental results on two large palmprint databases demonstrate the effectiveness of the proposed approach.KeywordsGraph MatchPalmprint ImagePalmprint RecognitionLocal Image RegionPalmprint DatabaseThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This paper presents a novel real-time palmprint recognition system for cooperative user applications. This system is the first one achieving noncontact capturing and recognizing palmprint images under unconstrained scenes. Its novelties can be described in two aspects. The first is a novel design of image capturing device. The hardware can reduce influences of background objects and segment out hand regions efficiently. The second is a process of automatic hand detection and fast palmprint alignment, which aims to obtain normalized palmprint images for subsequent feature extraction. The palmprint recognition algorithm used in the system is based on accurate ordinal palmprint representation. By integrating power of the novel imaging device, the palmprint preprocessing approach and the palmprint recognition engine, the proposed system provides a friendly user interface and achieves a good performance under unconstrained scenes simultaneously.

Because of their high recognition rates, coding-based approaches that use multispectral palmprint images have become one of the most popular palmprint recognition methods. This paper describes a new multispectral palmprint recognition method that aims to further improve the performance of coding-based approaches by focusing on the local binary pattern (LBP) filters and spiral moments features. The final feature map is derived through a staged process of creating a composite of spiral and LBP features by fusing them together and passing the features through the minimum redundancy maximum relevance transformers. Using Hamming distances, the inter- and intra-similarities of the palmprint feature maps are determined. The experimental technique was evaluated using the available data on the IITD, MSPolyU and PolyU PPDB databases. The results indicate that the method achieved high levels of accuracy in the identification and verification modes. Furthermore, this method outperforms the existing advanced techniques.

Fisherpalms based palmprint recognition