Hybrid local phase quantization and grey wolf optimization based SVM for finger vein recognition

Kapoor, Kamal K.; Rani, Shalli; Kumar, Munish; Chopra, Vinay; Brar, Gubinder Singh

doi:10.1007/s11042-021-10548-1

Cited by 49 publications

(10 citation statements)

References 48 publications

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“…The identification rates of each method are shown in Table 2 . When 600 images were used for the experiments, the recognition accuracy of PCLPP reached 0.99, which was significantly better than that of the literature [ 19 ]; when 3600 images were used for the experiments, our method was also better than that of the literature [ 1 ].…”

Section: Experimental Analysismentioning

confidence: 72%

“…We compared the proposed PCLPP with two other methods used in the literature, [ 1 , 19 ], using SDMULA-HMT as the input image set. The identification rates of each method are shown in Table 2 .…”

Section: Experimental Analysismentioning

confidence: 99%

“…Identity verification is required in daily life scenarios such as unlocking cell phones, logging into personal electronic accounts, using ATMs, and e-commerce. Traditional authentication technologies such as passwords and PINs [ 1 ] have some disadvantages, for instance, low security and being easy to forget and lose, making it difficult to meet the security needs in many application scenarios. Biometric identification technology [ 2 ] has received more and more attention in recent years because of its advantages, such as living identification, being difficult to steal, and having unique characteristics.…”

Section: Introductionmentioning

confidence: 99%

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A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections

Duan

Zhou

et al. 2022

Sensors

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In the field of biometric recognition, finger vein recognition has received widespread attention by virtue of its advantages, such as biopsy, which is not easy to be stolen. However, due to the limitation of acquisition conditions such as noise and illumination, as well as the limitation of computational resources, the discriminative features are not comprehensive enough when performing finger vein image feature extraction. It will lead to such a result that the accuracy of image recognition cannot meet the needs of large numbers of users and high security. Therefore, this paper proposes a novel feature extraction method called principal component local preservation projections (PCLPP). It organically combines principal component analysis (PCA) and locality preserving projections (LPP) and constructs a projection matrix that preserves both the global and local features of the image, which will meet the urgent needs of large numbers of users and high security. In this paper, we apply the Shandong University homologous multi-modal traits (SDUMLA-HMT) finger vein database to evaluate PCLPP and add “Salt and pepper” noise to the dataset to verify the robustness of PCLPP. The experimental results show that the image recognition rate after applying PCLPP is much better than the other two methods, PCA and LPP, for feature extraction.

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Section: Experimental Analysismentioning

confidence: 72%

Section: Experimental Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections

Duan

Zhou

et al. 2022

Sensors

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“…Then cosine similarity is analyzed between the images by projecting into transformed subspace, and the resultant score is compared with threshold value from Receiver Operator Characteristic (ROC) by performance measures. With the obtained result, it is decided whether the person belongs to the same family or not [15]. The face is an essential attribute considered in a security system.…”

Section: Literature Reviewmentioning

confidence: 99%

Criminal Persons Recognition Using Improved Feature Extraction Based Local Phase Quantization

Karuppanan¹,

Dhanalakshmi²

2022

Intelligent Automation &Amp; Soft Computing

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Facial recognition is a trending technology that can identify or verify an individual from a video frame or digital image from any source. A major concern of facial recognition is achieving the accuracy on classification, precision, recall and F1-Score. Traditionally, numerous techniques involved in the working principle of facial recognition, as like Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Subspace Decomposition Method, Eigen Feature extraction Method and all are characterized as instable, poor generalization which leads to poor classification. But the simplified method is feature extraction by comparing the particular facial features of the images from the collected dataset namely Labeled faces in the wild (LFW) and Olivetti Research Laboratory (ORL) dataset. In this paper, the feature extraction is based on local phase quantization with directional graph features for an effective optimal path and the geometric features. Further, Person Identification based deep neural network (PI-DNN) has proposed are expected to provide a high recognition rate. Various performance metrics, such as recognition rate, classification accuracy, accuracy, precision, recall, F1-score is evaluated. The proposed method achieves highperformance values when it is compared with other existing methods. The novelty of this paper explains in understanding the various features of different types of classifiers used. It is mainly developed to recognize the human faces in the crowd, and it is also deployed for criminal identification.

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“…Edge extraction operators have been widely used in ROI extraction [ 22 ]. However, there are a series of individuals, environments, devices, and other factors in the acquisition process, resulting in a large number of low-quality finger vein images, especially the disappearance of finger edges or the generation of weak edges, which have a great impact on the extraction of finger edges.…”

Section: Roi Extraction Combining the Kirsch Operator And The 3 ...mentioning

confidence: 99%

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity

Wang

Liu

et al. 2022

Journal of Healthcare Engineering

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Region of interest (ROI) extraction is a key step in finger vein recognition preprocessing. The current method takes the finger region in the vein image as the ROI, but this method cannot obtain better recognition accuracy because it only removes the background noise of the image and ignores factors such as the position and shape of the finger. To solve this problem, we limited the ROI to a fixed region between two finger joint cavities, proposed a new large receptive field gradient operator, and designed and implemented a new method for ROI extraction. It uses a large receptive field to search the target, which is similar to human vision, thus solving the problem of difficult ROI localization for images with large gradient areas. Moreover, for external factors such as noise and uneven illumination in the vein image, the interference factors can be eliminated by averaging them to a larger range with a larger size operator to improve the accuracy of the subsequent matching recognition. To verify the effectiveness of the proposed method, we conducted sufficient matching experiments on three public finger vein datasets. On various datasets, our method significantly reduced the identified EER value, with the lowest EER value reaching 0.96%. The experimental results show that the proposed ROI extraction method can effectively eliminate the influence of finger position, finger shape, and other factors on the subsequent recognition performance, accurately locate the finger joint cavity, and effectively improve the recognition performance.

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Hybrid local phase quantization and grey wolf optimization based SVM for finger vein recognition

Cited by 49 publications

References 48 publications

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections

A Finger Vein Feature Extraction Method Incorporating Principal Component Analysis and Locality Preserving Projections

Criminal Persons Recognition Using Improved Feature Extraction Based Local Phase Quantization

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity

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