A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN

Zhai, Yongjie; Cao, He; Cao, Lu; Ma, Hui; Gan, Junying; Zeng, Junying; Piuri, Vincenzo; Scotti, Fabio; Deng, Wenbo; Zhi, Yihang

doi:10.1007/978-3-319-97909-0_2

Cited by 23 publications

(10 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As far as we are concerned there are studies [34,35] that utilized CNN models for recognizing FKP images. In this paper, we address the FKP recognition issue and attempt to develop a new design for the recognition of the FKP image.…”

Section: Literature Reviewmentioning

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

Section: Literature Reviewmentioning

confidence: 99%

Deep Convolutional Neural Network for Finger-Knuckle-Print Recognition

Zohrevand

Imani

Ezoji

2021

IJE

View full text Add to dashboard Cite

show abstract

“…The third step of the work is to derive the descriptors [19] from the knuckle located image because, the key descriptors are used to match each person identities. The autoencoder utilizes the different layers such as input, hidden and output which helps to derive the features from located images unlike the autoencoder, sparse encoder [20] uses the greater number of hidden units compare to the input unit but minimum units are activated while extracting the features from the image. The feature extraction process includes the sparsity penalty concept in hidden layer h. The penalty process in layer h is mentioned as using Eq.…”

Section: Knuckle Feature Descriptor Extractionmentioning

confidence: 99%

Automatic Biometric System for Finger Knuckle Using Sparse Encoder Approaches

2021

Advances in Parallel Computing Technologies and Applications

View full text Add to dashboard Cite

Biometric recognition is one of the effective authentication techniques which is utilized in various applications for making the individual identification process. During the verification and authentication process different biometric features such as signature, ear, iris, face, palm, finger knuckle details are used to perform this process. Due to the easy acceptance of the palm surface, fine textures and stable features characteristics are helps to choose the finger knuckle feature for biometric process in this work. First the finger biometric features are collected from PolyU finger knuckle database. After that, the noise present in the images are eliminated using weighted median filter and the knuckle region is located with the help of the variational approach. After that key point descriptors are extracted using sparse autoencoder approach. Finally, the specific features are trained using compositional networks and features matching is performed by Chebyshev distance. The matching process authenticate the user whether they are authorized person or not. At last efficiency of the system is evaluated using MATLAB based experimental results such as false acceptance rate, equal error rate and false rejection rate.

show abstract

“…Lalithamani et al [24] introduced a new biometric authentication system based on major finger knuckle patterns by employing convolution neural networks (CNNs). The back-propagation algorithm with stochastic gradient descent and mini-batch learning has been used to train the CNNs, whereas Zhai et al [25] presented a new batch-normalized CNNs architecture for FKP recognition. The data augmentation techniques of random histogram equalization and dropout layers were implemented to prevent over fitting during training of the proposed scheme.…”

Section: Introductionmentioning

confidence: 99%

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

Attia

Akhtar

Chahir

2020

SIViP

View full text Add to dashboard Cite

The identification of individuals by their finger dorsal patterns has become a very active area of research in recent years. In this paper, we present a multimodal biometric personal identification system that combines the information extracted from the finger dorsal surface image with the major and minor knuckle pattern regions. In particular, first the features are extracted from each single region by BSIF (binarized statistical image features) technique. Then, extracted information is fused at feature level. Fusion is followed by dimensionality reduction step using PCA (principal component analysis) + LDA (linear discriminant analysis) scheme in order to improve its discriminatory power. Finally, in the matching stage, the cosine Mahalanobis distance has been employed. Experiments were conducted on publicly available database for minor and major finger knuckle images, which was collected from 503 different subjects. Reported experimental results show that feature-level fusion leads to improved performance over single modality approaches, as well as over previously proposed methods in the literature.

show abstract

A Novel Finger-Knuckle-Print Recognition Based on Batch-Normalized CNN

Cited by 23 publications

References 25 publications

Deep Convolutional Neural Network for Finger-Knuckle-Print Recognition

Deep Convolutional Neural Network for Finger-Knuckle-Print Recognition

Automatic Biometric System for Finger Knuckle Using Sparse Encoder Approaches

Feature-level fusion of major and minor dorsal finger knuckle patterns for person authentication

Contact Info

Product

Resources

About