Features-Level Fusion of Reflectance and Illumination Images in Finger-Knuckle-Print Identification System

Chaa, Mourad; Boukezzoula, Naceur-Eddine; Meraoumia, Abdallah

doi:10.1142/s0218213018500070

Cited by 12 publications

(5 citation statements)

References 6 publications

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“…The separated fingers are angle-normalized for subsequent image processing. The specific details of the algorithm are shown in [7], [9]and [10]The rotation formula of the finger image is as follows: cos sin cos sin…”

Section: Image Segmentationmentioning

confidence: 99%

Segmentation algorithm of digital dorsal joint pattern image based on morphological operation

Cao

Zhu

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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When using the non-contact method to collect the image of the back of the hand, in view of the problems that the different degrees of finger opening and closing affect the segmentation of the finger image, the area of interest of the knuckles of the knuckles is difficult to locate, and the image is easily affected by light, a knuckles of the knuckles of the knuckles based on morphological operations are proposed. Region of interest positioning method. In this method, a combination of opening and closing operations in morphological operations is used to construct an image mask of the back of the hand, and then the mask is used to shield the area of the back of the hand, and five images of the back of the finger are separated and angle-corrected. For the corrected image, the top-hat transformation is performed first, and then the method of combining the sliding window algorithm with image information entropy is used to locate the knuckle pattern area of the dorsum of fingers. Finally, LBP and SIFT are used for feature extraction, and support vector machine (SVM) is used as a classifier, and experimental verification is carried out on the self-built back of hand image database. The experimental results show that the proposed method can eliminate the influence of illumination and environmental noise, and can accurately and quickly locate and extract the region of interest of the dorsal knuckle pattern on the back of the hand image.

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Section: Image Segmentationmentioning

confidence: 99%

Segmentation algorithm of digital dorsal joint pattern image based on morphological operation

Cao

Zhu

2023

Eighth International Conference on Electronic Technology and Information Science (ICETIS 2023)

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“…Badrinath et al [21] combined SURF and SIFT to enhance the FKP texture images, and FKP recognition. The reflectance and illumination were extracted from each FKP image [22]. Then they used serial feature fusion to create a huge vector of feature for each individual.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Convolutional Neural Network for Finger-Knuckle-Print Recognition

Zohrevand

Imani

Ezoji

2021

IJE

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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.

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“…In the design step during a biometric system's implementation, an important question arises: on which biometric modality will we work? Currently, many biometric technologies have been developed; human hand-extracted features have confirmed their reliability, acceptability by the user, and low cost in all security applications [11,18] as a consequence of their simplicity and effectiveness in extracting features (even from low-resolution images) [6]. Palm-print modality is one of the human handextracted biometric technologies that has received growing attention recently [43].…”

Section: Introductionmentioning

confidence: 99%

Learning-Free Deep Features for Multispectral Palm-Print Classification

Bendjenna

Allah²,

Meraoumia³

2023

csci

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The feature extraction step is a major and crucial step in analyzing and understanding raw data as it has a considerable impact on the system accuracy. Unfortunately, despite the very acceptable results obtained by many handcrafted methods, they can have difficulty representing the features in the case of large databases or with strongly correlated samples. In this context, we proposed a new, simple and lightweight method for deep feature extraction. Our method can be configured to produce four different deep features, each controlled to tune the system accuracy. We have evaluated the performance of our method using a multispectral palmprint based biometric system and the experimental results, using the CASIA database, have shown that our method has high accuracy compared to many current handcrafted feature extraction methods and many well known deep learning based methods.

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Features-Level Fusion of Reflectance and Illumination Images in Finger-Knuckle-Print Identification System

Cited by 12 publications

References 6 publications

Segmentation algorithm of digital dorsal joint pattern image based on morphological operation

Segmentation algorithm of digital dorsal joint pattern image based on morphological operation

Deep Convolutional Neural Network for Finger-Knuckle-Print Recognition

Learning-Free Deep Features for Multispectral Palm-Print Classification

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