Secure and Efficient Outsourcing of PCA-Based Face Recognition

Zhang, Yushu; Xiao, Xiangli; Yang, Lu‐Xing; Xiang, Yong; Zhong, Sheng

doi:10.1109/tifs.2019.2947872

Cited by 77 publications

(38 citation statements)

References 40 publications

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“…As the core technology of machine vision, object extraction will be widely used. For example, in a machine vision system, fingerprints and facial features are detected, feature points and lines are extracted, and used for image matching and 3D modeling of camera measurement and suppression technology [30]. Effective target extraction is very important for advanced tasks in the intelligent analysis process, such as target classification, target tracking, and identification.…”

Section: A Image Target Extraction In Machine Visionmentioning

confidence: 99%

Retracted: Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Sun

Shi-hong

et al. 2020

IEEE Access

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With the gradual increase in the difficulty of competitive martial arts, athletes must complete fine, stable, high-quality and difficult movements in order to achieve excellent performance. The real-time extraction of martial arts movements is a topic that many martial arts enthusiasts care about. This research mainly discusses the real-time extraction and simulation of VR image targets of martial arts routine difficulty action technology. Considering the complex characteristics of martial arts movements, this article will analyze the preprocessing content of existing images. This includes image enhancement and image filtering, and uses median filtering methods to enhance the characteristics of the collected images. In this way, the visual effect of the original image can be improved, and the processed image will contribute to the subsequent segmentation. A new image segmentation method is proposed for the color model of the image. According to the H component of the HSV model representing the characteristics of chromaticity, the color image is transformed into the HSV model, and the H component is extracted. The histogram concept applies to H components. Based on the histogram of the H component, the segmentation threshold is determined, and the cropping target in the image is detected. Because the model space is very sensitive to color, VR technology is used to automatically determine the segmentation target. Combined with the above division methods, the automatic extraction of objects in the image is completed. The method of using VR technology for image extraction processing has high precision, and the error value is 3.92%<5%. The research results show that the VR segmentation results are good, suitable for image segmentation in complex backgrounds and automatic image extraction in complex backgrounds.

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Section: A Image Target Extraction In Machine Visionmentioning

confidence: 99%

Retracted: Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Sun

Shi-hong

et al. 2020

IEEE Access

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“…The full size of training samples is normalized to 48 × 128 pixels, and more than 1200 Haar features of each image are extracted. The hyperspectral image target detection algorithm is used to carry out face detection simulation experiment [45]. The experimental results are shown in Figure 4.…”

Section: Comparison Of Target Detection Algorithms In Hyperspectramentioning

confidence: 99%

Retracted: Image Target Detection Algorithm of Smart City Management Cases

2020

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With the rapid development and wide application of Internet technology, the use of the concept of "smart city" has been concerned and promoted. It is an inevitable way to strengthen the breadth and depth of urban services, and to move forward from digital to intelligent application. The purpose of this study is to further promote the small and medium-sized cities in China to improve the "smart city" management mode and promote the harmonious development of society, which has important reference and practical significance. In this paper, in-depth analysis of the background of smart city, different image target detection algorithms are studied. The infrared image target detection algorithm suppresses the background by high pass filter, and the correlation coefficient between the features is used as the fusion weight, and the weighted feature fusion of gray, area and centroid offset is carried out. Hyperspectral image target detection algorithm extracts some comprehensive indicators from the original data, and finally realizes the optimization of the algorithm. The mean filtering algorithm can reduce the influence of noise by preprocessing the image. Hog target detection algorithm describes the edge features of the object surface in areas such as graphics and image processing, and calculates the feature vector by the tilt direction of the specific block in the image. These algorithms have their own advantages and characteristics. The experimental results show that the accuracy and recall rate of the infrared image target detection algorithm after feature fusion are higher than other algorithms, the accuracy is higher than 6.3% of the original infrared image algorithm, and the recall rate is 5.4% higher than that of the untreated infrared image algorithm. Changing the m value of the main vector dimension will affect the target detection accuracy.

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“…In December 2015, Baidu Deep Learning Institute developed the unmanned vehicle technology [29], which realized the automatic driving of unmanned vehicles in various complicated road conditions. In March 2014, Professor Tang Xiaoou from the Chinese University of Hong Kong proposed a Gaussian Face-based algorithm for face recognition experiments on the LFW database with an accuracy of 98.52% [30,31]. In June of the same year, Professor Tang Xiaoou's team conducted a series of experiments based on the Deep ID algorithm, which improved the face recognition accuracy rate to 99.55%, promoted the development of face recognition in China [32].…”

Section: Introductionmentioning

confidence: 99%

Retracted: Content-Based E-Commerce Image Classification Research

Zhang

2020

IEEE Access

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The 21st century is the era of big data in the Internet. Online shopping has become a trend, and e-commerce has developed rapidly. With the exponential increase of the amount of commodity image data, the management of massive commodity image database restricts the development of e-commerce to some extent. In order to effectively manage goods and improve the accuracy and efficiency of product image retrieval, this paper uses content-based methods to classify e-commerce images. Aiming at the problems of insufficient classification accuracy and long classification training time in e-commerce image classification, an adaptive momentum learning rate based LBP-DBN training algorithm-AML-LBP-DBN and commodity image classification method based on image local feature multi-level clustering and image-class nearest neighbor classifier are proposed. By simulating the commodity identification dataset RPC, the results show that the proposed method has obvious advantages in the classification training time and classification accuracy of e-commerce images.

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Secure and Efficient Outsourcing of PCA-Based Face Recognition

Cited by 77 publications

References 40 publications

Retracted: Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Retracted: Martial Arts Routine Difficulty Action Technology VR Image Target Real-Time Extraction Simulation

Retracted: Image Target Detection Algorithm of Smart City Management Cases

Retracted: Content-Based E-Commerce Image Classification Research

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