FPR using machine learning with multi‐feature method

Kumar, Munish; Singh, Priyanka

doi:10.1049/iet-ipr.2017.1406

Cited by 11 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This model is based on a decision plane that defines decision boundaries. 16,17 Firstly; the training data is mapped into a high dimensional space which gives a hyperplane that separates one class of objects from another. If this hyperplane is mapped back into original dimensional space it may give a nonlinear classifier.…”

Section: Proposed Methodologymentioning

confidence: 99%

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Sonal¹,

Singh²,

Kant

2021

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

Interest in facial recognition hypotheses and algorithms has grown steadily over the last few decades. Video monitoring, criminal identification, building access control, and unmanned and autonomous vehicles are only a few examples of concrete applications that are becoming increasingly attractive to industry. Various techniques are being developed, including local, holistic, and hybrid approaches, which use only a few face image characteristics or the entire facial features to provide a face image description. Many methods have good results, if there are sufficiently representative training samples per person, in the face recognition system. Facial part finding and extraction show the utmost vital role in face and age recognition. In this research work a new algorithm is proposed for Face and Age Recognition (FAR) by using Discrete Wavelet Transform (DWT), Radial Basis Function Support Vector Machine (RBF-SVM) classifier, and Rotational Local Binary Pattern (RLBP). RLBP is utilized for the selection and extraction of features from the face image. In this algorithm, extract the face component like Nose, Mouth, Left and Right eye. In the preprocessing stage median filter is used to remove noises from the face image. By using this, there is an improvement in the feature extraction procedure. In pattern recognition, a basic errand is finding a picture from the picture parts. For the implementation of results FG-NET ((Face and Gesture Recognition Network) and AT&T datasets are used. The detection rate of face recognition has reached up to 92–98% and the detection rate for age recognition is 87%. The proposed algorithm is compared with SVM shows better over previous algorithms and also estimate the value of accuracy.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Sonal¹,

Singh²,

Kant

2021

International Journal of Electrical Engineering & Education

View full text Add to dashboard Cite

show abstract

“…With trending AI, Kho et al [7] proposed a machine learning-based model for spoof detection which gives excellent results compared to existing methods. Kumar et al [8] also used machine learning with multi-feature method and carried out experiments on FVC 2000-2004. This experiment gave accuracy of 98% on the FVC 2000-2004 databases.…”

Section: Svm Classifier For Spoof Detectionmentioning

confidence: 99%

Machine Learning Based Non Live Finger Print Detection

Subhashini¹,

Umamaheswari²

2023

IJSREM

View full text Add to dashboard Cite

In recent days, liveness detection of finger print image has become very essential in finger print recognition systems because fake finger prints are used in lieu of real finger prints. Many machine learning(ML) techniques have been widely used for non live finger print image detection because these techniques provide high accurate identification and also cost effective. These techniques also enhance the accuracy of classification of real and spoof finger print images. In this article , literature review is done about machine learning (ML) and its algorithms used for the detection of non live finger print. The main objective of this article is to compare and analyse various ML techniques used for spoof detection. It also provides an overview of performance merits and limitations of ML algorithms used in non live finger print detection . Index Terms— Non live finger print, liveness detection , Machine learning, anti spoofing, spoof detection

show abstract

“…In the same year, an FPGA implementation of a fast median filtering algorithm based on rank order was designed, which greatly exploits the parallelism operation principle of FPGAs. In the last two years, the number of FPGA applications of the algorithm has increased, and based on previous research, an enhancement algorithm based on statistical and logarithmic image processing is proposed [10]. The proposed method uses the fusion of multiple computed luminance channels with the statistical information of the color image channels obtained from the input color image to perform an adaptive color enhancement.…”

Section: Related Workmentioning

confidence: 99%

Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation

Wang

Yuan

2022

Advances in Mathematical Physics

View full text Add to dashboard Cite

This paper studies the processing of digital media images using a diffusion equation to increase the contrast of the image by stretching or extending the distribution of luminance data of the image to obtain clearer information of digital media images. In this paper, the image enhancement algorithm of nonlinear diffusion filtering is used to add a velocity term to the diffusion function using a coupled denoising model, which makes the diffusion of the original model smooth, and the interferogram is solved numerically with the help of numerical simulation to verify the denoising processing effect before and after the model correction. To meet the real-time applications in the field of video surveillance, this paper focuses on the optimization of the algorithm program, including software pipeline optimization, operation unit balancing, single instruction multiple data optimization, arithmetic operation optimization, and onchip storage optimization. These optimizations enable the nonlinear diffusion filter-based image enhancement algorithm to achieve high processing efficiency on the C674xDSP, with a processing speed of 25 posts per second for 640 × 480 size video images. Finally, the significance means a value of super pixel blocks is calculated in superpixel units, and the image is segmented into objects and backgrounds by combining with the Otsu threshold segmentation algorithm to mention the image. In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. By comparing the experimental results qualitatively and quantitatively, it is shown that the algorithm in this paper has an obvious practical effect on contrast enhancement of digital media images and has certain practicality and superiority.

show abstract

FPR using machine learning with multi‐feature method

Cited by 11 publications

References 30 publications

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Face and age recognition using three-dimensional discrete wavelet transform and rotational local binary pattern with radial basis function support vector machine method

Machine Learning Based Non Live Finger Print Detection

Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation

Contact Info

Product

Resources

About