Artificial database expansion based on hand position variability for palm vein biometric system

Stanuch, Maciej; Skalski, Andrzej

doi:10.1109/ist.2018.8577123

Cited by 3 publications

(5 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In both cases, it is significantly more than in the previous work where the LBP approach was employed for the task of feature extraction. The previous result that was achieved was on the level between 92% and 96% based on the different approaches while using the LBP [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…The previous work on this system was focused on a deterministic approach using Local Binary Patterns (LBP) as an element for feature extraction along with feature parametrization based on an elastic deformation. As a result, the TPR of 97.69% was achieved [ 31 ]. Since then, the database was expanded greatly, and now, it is more appropriate to fit the neural network methods, which seems a reasonable solution to the biometric verification and recognition problems.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network

Stanuch

Wodziński

Skalski

2020

Sensors

Self Cite

View full text Add to dashboard Cite

Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users’ veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER).

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network

Stanuch

Wodziński

Skalski

2020

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Biometrika merupakan teknologi pengenalan diri yang menggunakan bagian tubuh atau perilaku dari manusia [4]. Biometrika memiliki ciri kerja dengan mengukur karakteristik pembeda pada badan atau perilaku seseorang tersebut dengan membandingkan karakteristik yang sebelumnya telah disimpan pada suatu database [7]. Terdapat beberapa cara untuk biometrika umum yang sering dipakai untuk pengenalan diri, seperti sidik jari (fingerprint), selaput pelangi, (iris), wajah (face), suara (voice), tanda tangan (signature), geometri tangan (hand geometry) dan telapak tangan (palmprint) [8].…”

Section: Pendahuluanunclassified

Hand Human Recognition Berdasarkan Geometri Telapak Tangan Menggunakan Principal Component Analysis

2019

View full text Add to dashboard Cite

Self-recognition system is a system that can be used to recognize someone's identity that can be done automatically using a computer. Automatic self-recognition can be done by using body parts or human behavior is known as biometrics. Biometrics is a self-recognition technology that uses body parts or behavior from humans. There are several ways for general biometrics that are often used for self-recognition, such as fingerprints, iris, face, face, voice, signature, hand geometry, and palmprint. Hand geometry is one of the biometrics owned by humans that can describe the geometric structure of a person's hand. The system contained in this study is a palm recognition system that uses feature-based extraction based on Principal Component Analysis (PCA). This technique involves taking the main components of the palm database. To find out the accuracy of the palm recognition system designed in this study, a system trial has been carried out using 21 palm image images from the database. From the results of this test, the results of the system are 52.38% correctly recognizing input images.

show abstract

“…Typically, the vein pattern images used for people recognition include zones of interest like fingers, [2][3][4] palmar region, [5][6][7] dorsum of hand, [8][9][10][11] forearm, and wrist. 12,13 Most of the hand vein recognition systems 4,6,[8][9][10][11][12][13][14] require four steps: (1) image acquisition, (2) preprocessing of digital images that define the region of interest (RoI), (3) feature extraction of hand pattern, and (4) classification/matching, as shown in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, both techniques require high computational time. 18 On the other hand, the local binary pattern (LBP) 14,19 algorithm has been used for vein recognition, but whenever there are spatial and contrast changes during image acquisition, the performance of this description technique decreases. 19 Moment invariants also have been implemented for vein pattern description.…”

Section: Introductionmentioning

confidence: 99%

Zernike moment invariants for hand vein pattern description from raw biometric data

et al. 2019

View full text Add to dashboard Cite

We propose an invariant description method based on Zernike moments to classify hand vein patterns from raw infrared (IR) images. Orthogonal moments provide linearly independent descriptors and are invariant to affine transformations, such as translation, rotation, and scaling. A mathematical expression is given to derive a set of moment invariants. The obtained features have all the properties of moment invariants with the additional feature of image contrast invariance. For dorsal hand vein pattern acquisition, an IR imaging system is implemented. Also, a public database is used for a palm vein recognition task. A correct rate classification (CRC) above 99.9% is achieved using a set of rotation, scale, and intensity Zernike moment invariants. Additionally, multilayer perceptron and K-nearest neighbors are used as classifiers having as input data the Zernike normalized moments. A discriminative feature evaluation of the image moments allows the reduction of the number of descriptors while maintaining a high classification rate of 99%. The efficiency of the moment descriptors is evaluated in terms of accuracy and reduced computational cost by (a) avoiding the necessity of a preprocessing stage and (b) reducing the feature vector dimension. Experimental results show that Zernike moment invariants are able to achieve hand vein recognition without image preprocessing or image normalization with respect to change of size, rotation, and intensity.

show abstract

Artificial database expansion based on hand position variability for palm vein biometric system

Cited by 3 publications

References 18 publications

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network

Hand Human Recognition Berdasarkan Geometri Telapak Tangan Menggunakan Principal Component Analysis

Zernike moment invariants for hand vein pattern description from raw biometric data

Contact Info

Product

Resources

About