Fingerprint Recognition Based on Wavelet Transform and Ensemble Subspace Classifier

Rojas, Andres; Dolecek, Gordana Jovanovic

doi:10.1109/urucon53396.2021.9647176

Cited by 4 publications

(7 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…More recently, the project in [14] for biometric human footprint matching applied ensemble subspace discriminant classifiers combined with fuzzy logic. Finally, the work presented at URUCON 2021 reported in [9] is one of the newest fingerprint recognition systems based on an ensemble of Linear Discriminant Analysis (LDA) classifiers using the random subspace method. This system utilizes preprocessing to enhance the fingerprint images in conjunction with multiple domain feature extraction techniques.…”

Section: Motivation and Noveltymentioning

confidence: 99%

“…This system utilizes preprocessing to enhance the fingerprint images in conjunction with multiple domain feature extraction techniques. The novelty of this work is the application of multiple transforms for feature extraction of fingerprint images [9] to implement a fingerprint verification system simulated in MATLAB. This paper highlights essential details such as the preprocessing algorithm steps, multiple image processing techniques, and more insight into the feature extraction, especially texture descriptors.…”

Section: Motivation and Noveltymentioning

confidence: 99%

“…This approach was applied to extract information from the fingerprint images. More information about the Wavelet transform procedure is available in [9].…”

Section: Figure 2 Preprocessing Example Of the Input Fingerprint Imagesmentioning

confidence: 99%

“…This approach enhances the generalization of models and improves the classification results even using weak classifiers [8]. This paper presents an extended version of the fingerprint verification system based on the Wavelet transform and ensemble subspace discriminant classifier [9]. It contains a detailed description of the image processing techniques applied to the preprocessing algorithm and more information regarding the features computed to classify the fingerprint images.…”

Section: Introduction -mentioning

confidence: 99%

See 3 more Smart Citations

Fingerprint Verification System Based on DWT, Multiple Domain Feature Extraction, and Ensemble Subspace Classifier

Rojas

Dolecek

2022

Memoria investig. ing. (Facultad Ing., Univ. Montev.)

View full text Add to dashboard Cite

This paper describes a fingerprint verification system including preprocessing, Wavelet transform, feature extraction using multiple domains, and ensemble subspace discriminant classifier. The system is implemented in MATLAB using Wavelet Toolbox, Image Processing Toolbox, and Statistics and Machine Learning Toolbox. First, the motivation and novelty, followed by the review of the previous work, are presented. Next, all steps are described in detail. Three fingerprint databases from the literature are used. The proposed method's performance is compared with state-of-the-art techniques based on different classifiers utilizing the accuracy metric. The proposed algorithm achieves high accuracy at 97.5% for the DB3-FVC2000 subset.

show abstract

Section: Motivation and Noveltymentioning

confidence: 99%

Section: Motivation and Noveltymentioning

confidence: 99%

“…This approach was applied to extract information from the fingerprint images. More information about the Wavelet transform procedure is available in [9].…”

Section: Figure 2 Preprocessing Example Of the Input Fingerprint Imagesmentioning

confidence: 99%

Section: Introduction -mentioning

confidence: 99%

See 2 more Smart Citations

Fingerprint Verification System Based on DWT, Multiple Domain Feature Extraction, and Ensemble Subspace Classifier

Rojas

Dolecek

2022

Memoria investig. ing. (Facultad Ing., Univ. Montev.)

View full text Add to dashboard Cite

show abstract

“…Bilateral filter, fuzzy filter and adaptive median filter are the filters suitable to remove salt and pepper noises [11][12][13][14]. The texture properties of fingerprint are extracted using Haar Wavelet Transform [15]. Retina based identification is a biometric technology which makes use of unique blood vessel pattern of a user for recognition.…”

Section: Introductionmentioning

confidence: 99%

A Novel Biometric Key Security System with Clustering and Convolutional Neural Network for WSN

Balan

Ila

2022

Teh. vjesn.

View full text Add to dashboard Cite

Development in Wireless Communication technologies paves a way for the expansion of application and enhancement of security in Wireless Sensor Network using sensor nodes for communicating within the same or different clusters. In this work, a novel biometric key based security system is proposed with Optimized Convolutional Neural Network to differentiate authorized users from intruders to access network data and resources. Texture features are extracted from biometrics like Fingerprint, Retina and Facial expression to produce a biometric key, which is combined with pseudo random function for producing the secured private key for each user. Individually Adaptive Possibilistic C-Means Clustering and Kernel based Fuzzy C-Means Clustering are applied to the sensor nodes for grouping them into clusters based on the distance between the Cluster head and Cluster members. Group key obtained from fuzzy membership function of prime numbers is employed for packet transfer among groups. The three key security schemes proposed are Fingerprint Key based Security System, Retina Key based Security System, and Multibiometric Key based Security System with neural network for Wireless Sensor Networks. The results obtained from MATLAB Simulator indicates that the Multibiometric system with kernel clustering is highly secured and achieves simulation time less by 9%, energy consumption diminished by 20%, delay is reduced by 2%, Attack Detection Rate is improved by 5%, Packet Delivery Ratio increases by 6%, Packet Loss Ratio is decreased by 27%, Accuracy enhanced by 2%, and achieves 1% better precision compared to other methods.

show abstract

A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection

Sumalatha,

Prakasha,

Prabhu

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In the contemporary technological landscape, biometrics, encompassing the analysis of biological data, have gained significance. Biometrics is a methodology that utilize unique behavioral, physical, or morphological traits-such as speech, facial features, iris, fingerprint, retina, and signature for individual identification. Biometric technology has been successfully used in forensic science, security, and authorization systems. This review highlights understanding the classification, types of biometric traits and their comparisons, fingerprint recognition stands out as a reliable and widely adopted method due to its simplicity and cost-effectiveness, accuracy and robustness compared to others. Unimodal fingerprint biometric systems safeguard authentication information through the analysis of characteristic sequences and, face challenges such as vulnerability to spoof attacks, inter-class similarity, intra-class variation, non-universality, and noisy data. These challenges are addressed by multimodal fingerprint biometric systems, in which various biometric sources compensate for each other's limitations. The review focuses on the overview of unimodal and multimodal fingerprint biometric systems and the importance of fusion, advancements in data acquisition, preprocessing, feature extraction, matching algorithms, performance metrics, indexing, template protection, and addressing attacks in enhancing system security and reliability. The review paper sheds light on the intricate relationship between these elements, offering valuable insights into the current state and potential evolution of the field. The review paper highlights current challenges and suggests future research directions, emphasizing the necessity for continual advancements in fusion techniques, template protection methods, and novel defense mechanisms to effectively mitigate emerging threats in unimodal and multimodal fingerprint biometric systems.

show abstract

Fingerprint Recognition Based on Wavelet Transform and Ensemble Subspace Classifier

Cited by 4 publications

References 11 publications

Fingerprint Verification System Based on DWT, Multiple Domain Feature Extraction, and Ensemble Subspace Classifier

Fingerprint Verification System Based on DWT, Multiple Domain Feature Extraction, and Ensemble Subspace Classifier

A Novel Biometric Key Security System with Clustering and Convolutional Neural Network for WSN

A Comprehensive Review of Unimodal and Multimodal Fingerprint Biometric Authentication Systems: Fusion, Attacks, and Template Protection

Contact Info

Product

Resources

About