An efficient palmprint recognition method based on block dominat orientation code

Chen, Haipeng

doi:10.1016/j.ijleo.2015.07.031

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…A commonly employed local statistics approach involves partitioning the collected image into uniform blocks and aggregating the palmprint image features through the local characteristics of each small block. For instance, Chen et al [15] investigated multispectral images using block statistics, enhancing the recognition system's accuracy by combining features from various frequency bands. Minaee et al [16] applied Gabor transformation to extract meaningful information features in the spatial domain, achieving a commendable recognition effect based on block statistics.…”

Section: Related Workmentioning

confidence: 99%

LDBM-PFR: Local Directional Binary Pattern Mixed Filtering for 2D and 3D Palmprint Fusion Recognition

Sen Lin,

Man Li

et al. 2024

Journal of Computers

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<p>Single-dimensional palmprint images are susceptible to interference from noise, lighting, and various factors, leading to information loss and subpar recognition performance. To address the problem, a fusion recognition scheme of 2D and 3D palmprints based on Hybrid Filter Local Orientation Binary Pattern (HFLOBP) is proposed. Firstly, the HFLOBP algorithm is employed to extract 2D features and 3D Shape Index (SI) features respectively to improve the recognition effect. Secondly, Principal Component Analysis (PCA) is utilized to improve the recognition efficiency, to reduce the dimension of 2D features and 3D Surface Type (ST) features of palmprint, to improve the recognition efficiency. Finally, the multi-dimensional palmprint features are fused by Double Weight Threshold Algorithm (DWTA), and classified and recognized by Collaborative Representation (CR). Experimental results conducted on the 3D palmprint database of Hong Kong Polytechnic University reveal that, in comparison with alternative classification methods, the average recognition rate reaches 99.98%, with an average recognition time of 0.64 seconds. The proposed method not only demonstrates a commendable recognition effect but also meets the real-time requirements, underscoring its practical application value.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

LDBM-PFR: Local Directional Binary Pattern Mixed Filtering for 2D and 3D Palmprint Fusion Recognition

Sen Lin,

Man Li

et al. 2024

Journal of Computers

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“…For recognition they have employed multiclass SVM and nearest neighbor classifiers. Haipeng Chen et al [3] An hybrid hierarchical multispectral palmprint recognition model by developing block dominant orientation code to capture texture information successfully in spatial domain. Hierarchical matching and feature level fusion method based on block selection is used to increase the accuracy and security of the system.…”

Section: Review Of Literaturementioning

confidence: 99%

Texture Features to Evaluate Biometric Verification System using Handvein and Palmprint

Rangappa¹,

Naveena²,

Chethan³

et al. 2018

IJCA

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In recent years multimodal biometrics plays vital role in real life scenarios. We have proposed evaluated a biometric verification system of universal acceptable hand based modalities. We have used the Dorsal Handvein and Palmprint traits that are recently emerging traits in the multimodal biometrics field. We used the well know texture methods like LBP, LPQ and Gabor filter to extract texture features on Handvein and Palm print databases. We compare results of texture methods individually and also we worked on combinations of all the features on both the modalities and which modality performs better on texture descriptors. We have shown results using GAR (Genuine Acceptance Rate) v/s FAR (False Acceptance Rate) with the threshold benchmark values of FAR (0.01%, 0.1%, 1%) to illustrate the performance of verification rate. At the last we have tested with Multi-algorithmic system to evaluate robustness of our system. From our experimental results, it is clearly evident that the LPQ+Gabor combination texture feature is more suitable for both modalities.

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“…In addition, lots of feature-based methods were presented to implement palmprint recognition tasks. For instance, the coding-based method [11,12,13,14,15,16,17] has been extensively researched in the past decades. In these studies, the palmprint features were extracted by using the coding of some filtering results.…”

Section: Introductionmentioning

confidence: 99%

“…In these studies, the palmprint features were extracted by using the coding of some filtering results. The common coding methods include binarized statistical image features (BSIF) [13], double-orientation code (DOC) [14] and block dominate orientation code [17], etc. Minaee et al [18] proposed a palmprint recognition algorithm by using the deep scattering network which achieved fine recognition performance.…”

Section: Introductionmentioning

confidence: 99%

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

Cheng

Zhang

2019

Sensors

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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.

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An efficient palmprint recognition method based on block dominat orientation code

Cited by 7 publications

References 32 publications

LDBM-PFR: Local Directional Binary Pattern Mixed Filtering for 2D and 3D Palmprint Fusion Recognition

LDBM-PFR: Local Directional Binary Pattern Mixed Filtering for 2D and 3D Palmprint Fusion Recognition

Texture Features to Evaluate Biometric Verification System using Handvein and Palmprint

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

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