Fingerprints verification based on their spectrum

Kasban, H.

doi:10.1016/j.neucom.2015.07.030

Cited by 27 publications

(8 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It minimizes the identification error and maximizes the geometric margin. SVMs are the most suitable binary linear identification methods [40][41][42][43]. SVM works for two-class problems by separating the data by a separating hyperplane, as shown in Fig.…”

Section: Svm Classifiermentioning

confidence: 99%

DTLM-DBP: Deep Transfer Learning Models for DNA Binding Proteins Identification

Saber¹,

Khairuddin²,

Yusof³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

The identification of DNA binding proteins (DNABPs) is considered a major challenge in genome annotation because they are linked to several important applied and research applications of cellular functions e.g., in the study of the biological, biophysical, and biochemical effects of antibiotics, drugs, and steroids on DNA. This paper presents an efficient approach for DNABPs identification based on deep transfer learning, named "DTLM-DBP." Two transfer learning methods are used in the identification process. The first is based on the pre-trained deep learning model as a feature's extractor and classifier. Two different pre-trained Convolutional Neural Networks (CNN), AlexNet 8 and VGG 16, are tested and compared. The second method uses the deep learning model as a feature's extractor only and two different classifiers for the identification process. Two classifiers, Support Vector Machine (SVM) and Random Forest (RF), are tested and compared. The proposed approach is tested using different DNA proteins datasets. The performance of the identification process is evaluated in terms of identification accuracy, sensitivity, specificity and MCC, with four available DNA proteins datasets: PDB1075, PDB186, PDNA-543, and PDNA-316. The results show that the RF classifier, with VGG-Net pre-trained deep transfer learning features, gives the highest performance. DTLM-DBP was compared with other published methods and it provides a considerable improvement in the performance of DNABPs identification.

show abstract

Section: Svm Classifiermentioning

confidence: 99%

DTLM-DBP: Deep Transfer Learning Models for DNA Binding Proteins Identification

Saber¹,

Khairuddin²,

Yusof³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…In fingerprint recognition, fingerprints can be represented by key points (ridged intersections and ridged endpoints) in the image. These key points are called minutiae points which are used to match the characteristics of a pair of fingerprints to identify a person [9], [10], [40]. Similarly, minutiae points that contain geometric information are employed for finger-vein verification in many existing works [13], [14], [15], [16].…”

Section: A Minutiae Extractionmentioning

confidence: 99%

Finger-Vein Pattern Restoration With Generative Adversarial Network

et al. 2020

View full text Add to dashboard Cite

Finger-vein recognition technology has attracted more and more attention because of its high security and convenience. However, the finger-vein image capturing is affected by various factors, which results that some vein patterns are missed in acquired image. Matching minutiae features in such images ultimately degrades verification performance of the finger-vein recognition system. To overcome this problem, in this paper, a novel finger-vein image restoration approach is proposed to recover the missed patterns based on generative adversarial network (GAN), as the first attempt in this area. Firstly, we employ the segmentation algorithm to extract finger-vein network, which is further subject to thinning operation. Secondly, the resulting thinning image is taken as an input of a GAN model to restore the missed vein patterns. Thirdly, the minutiae points are extracted from restoration finger-vein pattern. Finally, we propose a matching approach for verification. Experimental results show that the proposed method can restore the missed vein pattern and reduce the equal error rate (EER) of the finger-vein verification system. INDEX TERMS Finger-vein recognition, vein restoration, generative adversarial network.

show abstract

“…Different biometric traits such as fingerprints, face, iris, voice, signature are used for automatic identification of an individual based on their physiological and behavioral characteristics. Fingerprint based recognition [3,4] is one of the most reliable way of biometric authentication because of their universality, distinctiveness and accuracy. However, the quality of the fingerprint image heavily affects the performance of the recognition algorithms [5].…”

Section: Introductionmentioning

confidence: 99%

Two-stage quality adaptive fingerprint image enhancement using Fuzzy C-means clustering based fingerprint quality analysis

Sharma

Dey

2019

Image and Vision Computing

View full text Add to dashboard Cite

Fingerprint recognition techniques are immensely dependent on quality of the fingerprint images. To improve the performance of recognition algorithm for poor quality images an efficient enhancement algorithm should be designed. Performance improvement of recognition algorithm will be more if enhancement process is adaptive to the fingerprint quality (wet, dry or normal). In this paper, a quality adaptive fingerprint enhancement algorithm is proposed. The proposed fingerprint quality assessment algorithm clusters the fingerprint images in appropriate quality class of dry, wet, normal dry, normal wet and good quality using fuzzy c-means technique. It considers seven features namely, mean, moisture, variance, uniformity, contrast, ridge valley area uniformity and ridge valley uniformity into account for clustering the fingerprint images in appropriate quality class. Fingerprint images of each quality class undergo through a two-stage fingerprint quality enhancement process. A quality adaptive preprocessing method is used as front-end before enhancing the fingerprint images with Gabor, short term Fourier transform and oriented diffusion filtering based enhancement techniques. Experimental results show improvement in the verification results for FVC2004 datasets. Significant improvement in equal error rate is observed while using quality adaptive preprocessing based approaches in comparison to the current stateof-the-art enhancement techniques.

show abstract

Fingerprints verification based on their spectrum

Cited by 27 publications

References 26 publications

DTLM-DBP: Deep Transfer Learning Models for DNA Binding Proteins Identification

DTLM-DBP: Deep Transfer Learning Models for DNA Binding Proteins Identification

Finger-Vein Pattern Restoration With Generative Adversarial Network

Two-stage quality adaptive fingerprint image enhancement using Fuzzy C-means clustering based fingerprint quality analysis

Contact Info

Product

Resources

About