Correction to: Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Baskar, M.; Devi, R. Renuka; Ramkumar, J.; Kalyanasundaram, P.; Suchithra, M.; Amutha, B.

doi:10.1007/s11063-022-10888-5

Cited by 2 publications

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“…In order to address the impact of forged fingerprints in biometric-based security systems, Baskar M and other scholars proposed a region-centered detail propagation measurement-based method to detect forged fingerprints, which utilizes a multistage Gabor filter to remove the noise points, and then converts the enhanced image into a number of integral images. The results indicated that the method effectively improved the accuracy of forged fingerprint detection [11]. To design an effective method that can accurately detect in-depth forged images or videos, the research team of Arunkumar P M proposed to utilize deep learning techniques and introduced a fuzzy Fisher face model with capsule biplots to detect different types of fake images or videos.…”

Section: Related Workmentioning

confidence: 99%

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Image Content Forgery Detection Model Combining PSO and SVM in Electronic Data Forensics

Liao,

Lei

2024

IJCAI

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With the rapid progress of information technology, the Internet is saturated with copious amounts of data and visuals. However, with the widespread availability of different image editing software, counterfeit image content arises periodically. To tackle image content forgery, the research is founded on the Gaussian mixture distribution similarity measure image forgery detection algorithm. The image classifier underwent training through encoding its underlying features and utilizing the encoded data as inputs for the support vector machine. Optimization of the support vector machine was performed simultaneously using the improved particle swarm optimization algorithm. The results indicated that the SVM-based image content forgery detection model, which employed improved particle swarm optimization, achieved a detection rate of 94.89% and processed the images in 22.06 milliseconds. In summary, the study of an image content forgery detection model that combines improved particle swarm optimization and support vector machine in electronic data forensics has resulted in a high detection accuracy.Povzetek: Model za zaznavanje ponaredb slik združuje PSO in SVM za forenziko elektronskih podatkov. Uporablja izboljšani algoritma rojev delcev in podpornih vektorjev.

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Section: Related Workmentioning

confidence: 99%

“…In equation (10),  denotes the insensitive error and the insensitive loss function satisfies the obtained constraints as shown in equation (11).…”

Section: Icfd-svm Based On Improved Pso Optimizationmentioning

confidence: 99%

Image Content Forgery Detection Model Combining PSO and SVM in Electronic Data Forensics

Liao,

Lei

2024

IJCAI

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“…To overcome the current challenges in recognizing the fingerprint of a person with imperfect and overlapping fingerprints, the proposed method introduced an enhanced recognition system that details the fingerprint of the individual in an effective way [13,14]. This suggested technique addresses the current difficulties in identifying a person from fingerprint scans that are overlapping and of poor quality [15]. As a result, the focus of this research is on the feature identification and detection of the pertinent elements in tiny fingerprint image identification.…”

Section: Introductionmentioning

confidence: 99%

An Effective Finger-Print Validation and Identification Using A-KAZE and SURF Algorithm

2024

IJIES

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The identification of users through their fingerprints has been widely used in various applications such as biometrics, border control identification, payment gateways and so on. But a proper fingerprint validation system remains a major issue in precise detection and takes more time to process the fingerprints. To overcome these fore mentioned issues, this manuscript introduced an effective fingerprint validation system using the data collected from FVC 2000 DB1, FVC 2002 DB1 and FVC 2004 datasets. The pre-processing is supported for image enhancement using CLAHE algorithm equalization and normalization. Additionally, the signal images are effectively extracted by applying the A-KAZE and SURF extracted algorithm which helps to reduce the time required for feature extraction and description of the identification process. At the final stage, random sample consensus (RANSAC) algorithm is applied to identify and validate the fingerprint effectively with better accuracy. The elapse time of the proposed approach is 3.09 s whereas the elapse time of the automatic fingerprint-based authentication framework is 4.03 s and minutiae triangulation technique is 3.55 s. Similarly, when the proposed approach is evaluated with FVC 2004 dataset, the elapse time is 5.20 ms whereas the existing scale-invariant feature transform (SIFT) detector obtained elapse time of 4.35 s.

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Correction to: Region Centric Minutiae Propagation Measure Orient Forgery Detection with Finger Print Analysis in Health Care Systems

Cited by 2 publications

References 0 publications

Image Content Forgery Detection Model Combining PSO and SVM in Electronic Data Forensics

Image Content Forgery Detection Model Combining PSO and SVM in Electronic Data Forensics

An Effective Finger-Print Validation and Identification Using A-KAZE and SURF Algorithm

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