Dorsal Hand Vein Recognition by Convolutional Neural Networks: Feature Learning and Transfer Learning Approaches

Al-johania, Norah; Elrefaei, Lamiaa A.

doi:10.22266/ijies2019.0630.19

Cited by 28 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To identify the finger vein, a CNN based deeplearning approach is utilized in [18]. CNN based feature learning and transfer method has been applied to the hand dorsal vein [19]. As the deep learning approach required huge data to train the model, CNN based palm vein authentication was employed with pixelwise visibility-aware multi-view stereo network (PVSNet) architecture to train the model [20].…”

Section: Literature Reviewmentioning

confidence: 99%

A novel multimodal hand database for biometric authentication

2022

IJATEE

View full text Add to dashboard Cite

Automated person identification through a biometric device is the most popular technology for individual authentication. Recognition is based on the behavioral or physical characteristics such as face, fingerprints, voice, stride, and signature [1]. These characteristics are known as biometric cues or biometric modalities. For the past several years, these biometrics cues have been used in applications ranging from less security to high security [2−5].Due to the defensive nature of multimodal biometrics in terms of security and robustness, it is preferred over unimodal biometric systems [6].

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A novel multimodal hand database for biometric authentication

2022

IJATEE

View full text Add to dashboard Cite

show abstract

“…A CNN based deep-learning method for finger-vein identification similar to [31] has been used [32]. CNN feature learning and transfer approach on hand dorsal vein was performed in [33]. Palm vein authentication using CNN with PVSNet architecture for training to deal with the need for huge amount of training data in deep learning [34].…”

Section: Literature Reviewmentioning

confidence: 99%

FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

2020

View full text Add to dashboard Cite

Multimodal biometric systems are preferred as a defense compared to unimodal systems. This study introduces an open access multimodal vein database named FYO with each letter dedicated to each author's name. The database involves three biometric traits; palm vein, dorsal vein and wrist vein of the same individuals, to explore and enhance research in the area of using these traits to create a spoof-proof multimodal authentication system. The vein images of FYO are acquired using medical vein finder in a controlled environment. Comparisons are performed to show the differences with the existing well known databases and the state-of-the-art recognition algorithms. Hand-crafted feature extractors such as Binarized Statistical Image Features (BSIF), Gabor filter and Histogram of Oriented Gradients (HOG) are applied to show the viability of the vein datasets. Additionally, a deep learning based Convolutional Neural Networks (CNN) architecture is proposed with two models using decision-level fusion of palmar, dorsal and wrist biometric traits on vein images. Unimodal systems, multimodal systems and the proposed architecture are tested on several vein datasets including palmar, dorsal and wrist vein images. Experimental results based on accuracy and computation time on our FYO datasets showed competitive output with that of other databases such as Tongji Contactless Palm Vein database, VERA, PUT, Badawi and Bosphorus hand vein databases. Moreover, the proposed CNN architecture on three vein biometric traits show superior performance compared to hand-crafted methods. INDEX TERMS Data fusion, deep learning, hand-crafted features, vein recognition, multimodal biometrics.

show abstract

“…The stages of data collection are carried out manually, namely by using a program designed to collect facial data from each homeowner consisting of 5 people where the total data is 1100 data which will then be divided 1040 for training data and 60 data is used for validation during training by doing the facial augmentation process starts from shifting 10-15 degrees with various expressions [16]. The results of data collection can be seen in Fig.…”

Section: A Homeowner Face Data Collectionmentioning

confidence: 99%

Home Security System with Face Recognition based on Convolutional Neural Network

Irjanto¹,

Surantha²

2020

IJACSA

View full text Add to dashboard Cite

Security of house doors is very important and becomes the basis for the simplest and easiest security and sufficient to provide a sense of security to homeowners and along with technological developments, especially in the IoT field, which makes technological developments in locking house doors have developed a lot like locking house doors with faces and others. The development of facial recognition systems has also developed and has been implemented for home door locking systems and is an option that is quite simple and easy to use and is quite accurate in recognizing the face of homeowners. The development of the CNN method in facial recognition has become one of the face recognition systems that are easy to implement and have good accuracy in recognizing faces and has been used in object recognition systems and others. In this study, using the CNN Alexnet facial recognition system which is implemented in a door locking system, data collection is done by collecting 1048 facial data on the face of the homeowner using a system which is then used to train machine learning where the results are quite accurate where the accuracy is the result is 97.5% which is quite good compared to some other studies. The conclusion is the CNN Alexnet method can perform facial recognition which is quite accurate which can be implemented on the IoT device, namely, the Raspberry Pi.

show abstract

Dorsal Hand Vein Recognition by Convolutional Neural Networks: Feature Learning and Transfer Learning Approaches

Cited by 28 publications

References 14 publications

A novel multimodal hand database for biometric authentication

A novel multimodal hand database for biometric authentication

FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

Home Security System with Face Recognition based on Convolutional Neural Network

Contact Info

Product

Resources

About