A distinctive landmark-based face recognition system for identical twins by extracting novel weighted features

Mousavi, Shokoufeh; Charmi, Mostafa; Hassanpoor, Hossein

doi:10.1016/j.compeleceng.2021.107326

Cited by 6 publications

(5 citation statements)

References 13 publications

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“…According to Figure 5, none of the six algorithms recognized the chin position landmark of the person in the fat reduction motion image. However, the number of landmarks of the fat reduction motion image is 1, which is not recognized by the proposed algorithm, the number of landmarks of the fat reduction motion image is 5, which is not recognized in literature [5], the number of landmarks of the fat reduction motion image is 3, which is not recognized in literature [6], the number of landmarks of the fat reduction motion image is 4, which is not recognized in literature [7], the number of landmarks of the fat reduction motion image is 2, which is not recognized in literature [8], and the number of landmarks of the fat reduction motion image is 4, which is not recognized in literature [9].…”

Section: Resultsmentioning

confidence: 96%

“…Using the number of recognition landmarks as an index, the effect of the proposed algorithm on recognizing the landmarks of fat reduction motion images is tested. At the same time, it is compared with literature [5] algorithm, literature [6] algorithm, literature [7] algorithm, literature [8] algorithm, and literature [9] algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…e automatic recognition algorithm of a circular landmark in literature [5], the water gauge mark recognition algorithm in literature [6], the image mark feature adaptive recognition algorithm in literature [7], the mark automatic recognition algorithm in literature [8], the motion image sequence target recognition algorithm in literature [9], and the proposed algorithm are compared, and the practical application effects of different algorithms are verified through different experimental indexes:…”

Section: Evaluation Indexmentioning

confidence: 99%

“…However, this method can only recognize facial features and is not accurate for image landmark recognition pairs. Mousavi et al [7] proposed a landmark-based face recognition algorithm. e new features are proposed based on the number of key points and the most significant landmark regions of the face obtained by the improved scale-invariant feature transformation algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Recognition for Fat Reduction Motion Image Landmark Using Computer Vision

Yang

Wang

et al. 2022

Mobile Information Systems

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To solve the problems existing in the traditional algorithm, such as image quality, the ability to predict the coordinate of the motion fat reduction image, the Intersection over Union (IOU) of the estimated coordinate of the landmark, the peak signal-to-noise ratio (PSNR) of the image, and the recognition accuracy of the image landmark, an automatic recognition algorithm for fat reduction motion image landmark using computer vision is proposed. The OV7725 camera is used to obtain the fat reduction motion image, and the histogram equalization method is used to enhance the fat reduction motion image. After that, the estimated coordinates of the landmark are obtained by the automatic recognition model of the landmarks of the fat reduction motion image. According to the coordinates of the landmark, after using a field programmable gate array to segment the image of the fat reduction motion image, the incremental updated freeman chain code extracts the contour of the landmark of the fat reduction motion image through 8-neighborhood retrieval, so as to realize the automatic recognition of landmark. The results show that the fat reduction motion image enhanced by the algorithm is clear and the contrast is obvious. The ability to predict the coordinates of the landmark of the fat reduction image is strong. The IOU of the estimated landmark coordinates is about 0.96. The processed image has a high PSNR, which can effectively recognize the landmark of the fat reduction motion image, and the recognition accuracy is about 97%, which has certain value in the field of image recognition and can improve the fat reduction movement normality.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Evaluation Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Recognition for Fat Reduction Motion Image Landmark Using Computer Vision

Yang

Wang

et al. 2022

Mobile Information Systems

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“…Shokoufeh Mousavi et al [11] presented a Distinctive Landmark -centered FR (DLFR) system to diminish the issues. Grounded on the number of key points acquired as of a modified SIFT together with the most idiosyncratic face"s landmark region, novel features were generated.…”

Section: Literature Surveymentioning

confidence: 99%

An Enhanced Face Detection System using A Novel FIS-CDNN Classifier

S¹,

Rajashekararadhya²

2022

IJACSA

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A computer application that can detect, track, identify or verify human faces as of an image or video capture utilizing a digital camera is Face Recognition (FR). Few challenges like low-resolution images, aging, uncontrolled pose, illumination changes, and poor lighting conditions are not tackled even though huge advancement has been created in the Face Detection and Recognition (FDR) domain. Utilizing the Modified Tiny Face Detection (MTFD) and Fuzzy Interference System -Convolutional Deep Neural Network (FIS-CDNN) classifier, a Face Recognition System (FRS) was proposed here to tackle all complications. Primarily, Gamma correction -Based Histogram Equalization (GBHE) technique is utilized to augment the image's input in the pre-processing phase. The MTFD was employed to detect the face. Following that, the features are extracted. The Improved Chehra (IC) landmark extraction method was employed to retrieve the landmark features. And finally, the Geometric Features (GFs) are extracted. Later, the Gaussian -centered Spider Monkey Optimization (GSMO) Algorithm was employed to choose the vital features. To recognizing the face, the chosen features are fed into the FIS-CDNN classifier. When analogized to the prevailing models, it is concluded via the experiential outcomes that higher accuracy was attained by the proposed method.

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