Palmprint identification performance improvement via patch-based binarized statistical image features (Erratum)

Bendjoudi, Salim; Bourouba, Hocine; Doghmane, Hakim; Messaoudi, Kamel; Bourennane, El-Bay

doi:10.1117/1.jei.28.5.059802

Cited by 3 publications

(3 citation statements)

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“…e art color extraction technique is enhanced based on the style extraction of works of art, and a color extraction approach based on color characteristics is provided. e color feature area of works of art is segmented using the sparse dispersed point rearrangement method [18]. Within the search radius of R, the works of art with color characteristics are block segmented, and the appearance texture information feature is retrieved.…”

Section: Feature Information Fusion Of Work Ofmentioning

confidence: 99%

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

Yao

Sohail

2022

Mathematical Problems in Engineering

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In order to improve the style extraction and extraction ability of works of art, a color extraction method based on color features is proposed. The color feature extraction method is used to extract the style of visual works of art. The color feature region of works of art is segmented combined with a sparse scattered point reorganization method. Texture tracking and matching method is used for information fusion of works of art, combined with corner detection, three-dimensional edge contour feature detection method to realize texture filling and automatic rendering of art color extraction to improve art graphics’ color visual feature expression ability. The color feature data method is used for visual feature sampling and equalizing works of art. According to the equilibrium configuration results, the fuzzy clustering method is used to extract the color style of works of art to improve the style extraction and extraction identification ability of works of art. The simulation results show that this method has high accuracy in color extraction of works of art. It has a good effect on the extraction of art creation style and improves the ability of three-dimensional extraction and automatic extraction of art.

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Section: Feature Information Fusion Of Work Ofmentioning

confidence: 99%

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

Yao

Sohail

2022

Mathematical Problems in Engineering

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“…In a recent study [32], the authors compared the performance of pretrained CNNs for feature extraction in touchless palmprint recognition. Specifically, they employed pretrained AlexNet, VGG-16, and VGG-19 networks to extract features from touchless palmprint images.…”

Section: Deep-learning Approchesmentioning

confidence: 99%

“…To validate the superiority of our proposed model, we conducted comparisons with several recently popular methods, including encoding-based methods such as PalmCode [12], Competitive Code [13], PlamNet [17] and CR-CompCode [18], local descriptor-based methods like HOG [24], LDP [25], LMDP [30] and LMTrP [31] , as well as deep learning-based methods such as AlexNet [33], VGGNet [32], ResNet-18 [54] and PCANet [34]. Additionally, we also compared our proposed method with two recent competitive neural network-based approaches, namely CompNet [35] and CO3Net [36].…”

Section: Recognition Performancementioning

confidence: 99%

CECNet: Coordinate Encoding Competitive Neural Network For Palm Vein Recognition by Soft Large Margin Centralized Cosine Loss

Zhang,

Li,

Wang

et al. 2023

IEEE Access

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Palm vein recognition plays a crucial role in identity verification, requiring highly discriminative features. However, for touchless palm vein datasets, selecting a suitable and fixed Region of Interest (ROI) is challenging due to variations in capture scales and anisotropy. Moreover, manually annotating ROIs for each palm vein image is a time-consuming and labor-intensive task. To address these challenges, the method based on competitive mechanism is currently a popular approach for palm vein recognition. However, traditional competitive mechanisms only focus on selecting winners from different channels without considering the spatial information of features. In this paper, we reformulate the traditional competition mechanism and propose a Coordinate Encoding Competitive Neural Network (CECNet). Our method takes into account the spatial competition relationship between features, which means we pay attention to features of different directions and scales. We also perform spatial encoding on the competitive features to extract a more comprehensive set of competitive features. To extract the textures, the CECNet employs three parallel Adaptive Gabor Filter Encoders (AGFEs) to learn features of different directions and scales, effectively capturing the variations present in palm vein images. To enhance feature discrimination, the Soft Large Margin Centralized Cosine Loss (SLMCC) Loss function is utilized, taking into account with intra-class similarity and introducing centralized cosine similarity to achieve better interclass separation. By optimizing this loss function, the network learns to prioritize and rank features based on their importance. Experimental results on public palm vein datasets demonstrate the effectiveness of the proposed approach.

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Touchless Palmprint Identification Based on Patch Cross Pattern Representation

Doghmane

Messaoudi

Korba

et al. 2021

Lecture Notes in Electrical Engineering

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Over the last decade, palmprint recognition has been studied for many problems and applications. It has become one of the most well-known biometric recognition system. Its success is due to the rich features that can be extracted and exploited from the palmprint images captured by contact or contactless device. This paper presents a new representation based on textural structure of human palms for touchless palmprint identification. This representation method is called Patch Cross Pattern (PCP), which relies mainly on cross pattern encoder and the nonoverlapping decomposition method. The feature vector is built using Cross Pattern (CP) encoder to capture the textural structure of palmprint image. Then, the nonoverlapping decomposition on both directions is applied. Next, the feature vector representation of each palmprint image is constructed by concatenating all normalized histograms calculated at each patch. In addition, the reduced version of the PCP called R-PCP is obtained using whitened linear discriminant analysis. Finally, a Knearest neighbor classifier is used for palmprint identification. The proposed system is successfully applied to IIT Delhi and CASIA touchless databases. Results show that, the proposed representation provides a significant performance improvement compared to the recent state-of-the-art in terms of accuracy.

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Palmprint identification performance improvement via patch-based binarized statistical image features (Erratum)

Abstract: identification performance improvement via patch-based binarized statistical image features (Erratum),"

Cited by 3 publications

References 0 publications

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

Design of Artistic Creation Style Extraction Model Based on Color Feature Data

CECNet: Coordinate Encoding Competitive Neural Network For Palm Vein Recognition by Soft Large Margin Centralized Cosine Loss

Touchless Palmprint Identification Based on Patch Cross Pattern Representation

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