A biometric system based on Gabor feature extraction with SVM classifier for Finger-Knuckle-Print

Muthukumar, A.; Kavipriya, A.

doi:10.1016/j.patrec.2019.04.007

Cited by 40 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed method of human EAR recognition has performed the finest verification results on the EAR database IIT Delhi, with the equal error rate and accuracy of 88%. Table 5 Performance analysis of multi-model approaches Descriptor and biometrics Algorithm Modality FAR, % FRR, % GAR, % finger print and Iris [38] fuzzy vault based approach multimodal 0.01 1.8 98.2 finger print and iris [17] frequency-based approach multimodal 0 5.7 94.2 finger print and FKP [39] k…”

Section: Resultsmentioning

confidence: 99%

Retracted: Innovative approach for multimodal fusion recognition based feature extraction using band‐limited phase‐only correlation and discrete orthonormal Stockwell transform

Kavipriya

Muthukumar

2020

IET Image Processing

Self Cite

View full text Add to dashboard Cite

Biometrics is the bureau of science that helps to measure the individual's features by utilising their behavioural and physiological characteristics. Since years ago biometric technology are contemplated to be a unique tool for other security purposes. Inauspicious biometrics, Ear recognition, and Finger Knuckle Print have been enchanted as a booming analysis with interests among several researchers in modern periods. It has an ample range of private and other law enforcement applications. A combination of multiple human attributes is authenticated and considered to be a competent strategy in case of a multimodal Personal authentication system. In this paper extracting the local and global features via the structure of the timefrequency domain has been studied. This proposed scheme exploits the analysis of dual biometric modalities i.e. Ear and Finger Knuckle Print which are carried out at the stage of feature-level fusion. The feature Extraction two biometric patterns are obtained by generating the Local and Global feature information that helps in refining the alignment of dual biometric images in matching i.e. Discrete Orth normal Stockwell Transform-Ear recognition and Band Limited phase-only correlation with Finger Knuckle Print. Experiment results conducted with these FKP and EAR are demonstrated in improving recognition of efficient accuracy.

show abstract

Section: Resultsmentioning

confidence: 99%

Retracted: Innovative approach for multimodal fusion recognition based feature extraction using band‐limited phase‐only correlation and discrete orthonormal Stockwell transform

Kavipriya

Muthukumar

2020

IET Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…e first and second principal components of the images and the corresponding GLCM mean values obtained by the recursive 2 Complexity feature elimination are important variables for classification. Muthukumar and Kavipriya [28] used SVM classifier for information extraction, and after adding the multiscale texture features extracted by the Gabor filter, the total classification accuracy increased from 93.82% to 97.40%. e advantage of the convolutional neural network is that it can process the input multidimensional image directly.…”

Section: Related Workmentioning

confidence: 99%

Virtual Reality Video Image Classification Based on Texture Features

Qin

2021

Complexity

View full text Add to dashboard Cite

As one of the most widely used methods in deep learning technology, convolutional neural networks have powerful feature extraction capabilities and nonlinear data fitting capabilities. However, the convolutional neural network method still has disadvantages such as complex network model, too long training time and excessive consumption of computing resources, slow convergence speed, network overfitting, and classification accuracy that needs to be improved. Therefore, this article proposes a dense convolutional neural network classification algorithm based on texture features for images in virtual reality videos. First, the texture feature of the image is introduced as a priori information to reflect the spatial relationship between pixels and the unique characteristics of different types of ground features. Second, the grey level cooccurrence matrix (GLCM) is used to extract the grey level correlation features of the image in space. Then, Gauss Markov Random Field (GMRF) is used to establish the statistical correlation characteristics between neighbouring pixels, and the extracted GLCM-GMRF texture feature and image intensity vector are combined. Finally, based on DenseNet, an improved shallow layer dense convolutional neural network (L-DenseNet) is proposed, which can compress network parameters and improve the feature extraction ability of the network. The experimental results show that compared with the current classification method, this method can effectively suppress the influence of coherent speckle noise and obtain better classification results.

show abstract

“…Then they used serial feature fusion to create a huge vector of feature for each individual. The Gabor filter was utilized for extracting desired features [8]. They also used Hamming distance along with Support Vector Machine (SVM) for reducing False Acceptance Rate (FAR).…”

Section: Literature Reviewmentioning

confidence: 99%

“…[1]. In the past decades, many studies have investigated the advantages of some biometric characteristics, including face [2], iris [3], fingerprint [4], palm-print [5], hand geometry [6], finger-vein [7], and Finger Knuckle Print (FKP) [8]. Compared to different types of biometrics, hand-based biometrics has received significant attention in recent years [1].…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network for Finger-Knuckle-Print Recognition

Zohrevand

Imani

Ezoji

2021

IJE

View full text Add to dashboard Cite

Finger-Knuckle-Print (FKP) is an accurate and reliable biometric in compare to other hand-based biometrics like fingerprint because of the finger's dorsal region is not exposed to surfaces. In this paper, a simple end-to-end method based on Convolutional Neural Network (CNN) is proposed for FKP recognition. The proposed model is composed only of three convolutional layers and two fully connected layers. The number of trainable parameters hereby has significantly reduced. Additionally, a straightforward method is utilized for data augmentation in this paper. The performance of the proposed network is evaluated on Poly-U FKP dataset based on 10-fold cross-validation. The best recognition accuracy, mean accuracy and standard deviation are 99.83%, 99.18%, and 0.76, respectively. Experimental results show that the proposed method outperforms the state-of-the-arts in terms of recognition accuracy and the number of trainable parameters. Also, in compare to four fine-tuned CNN models including AlexNet, VGG16, ResNet34, and GoogleNet, the proposed simple method achieved higher performance in terms of recognition accuracy and the numbers of trainable parameters and training time.

show abstract

A biometric system based on Gabor feature extraction with SVM classifier for Finger-Knuckle-Print

Cited by 40 publications

References 15 publications

Retracted: Innovative approach for multimodal fusion recognition based feature extraction using band‐limited phase‐only correlation and discrete orthonormal Stockwell transform

Retracted: Innovative approach for multimodal fusion recognition based feature extraction using band‐limited phase‐only correlation and discrete orthonormal Stockwell transform

Virtual Reality Video Image Classification Based on Texture Features

Deep Convolutional Neural Network for Finger-Knuckle-Print Recognition

Contact Info

Product

Resources

About