A Face Detection Method Based on Image Processing and Improved Adaptive Boosting Algorithm

Zhang, Wanli; Li, Xianwei; Song, Qixiang; Lu, Wei

doi:10.18280/ts.370306

Cited by 6 publications

(5 citation statements)

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“…Wu et al [ 31 ] presented a tweaked CNN (TCNN), which relies on a mixed Gaussian model to cluster the features on different layers, and concluded that a deeper network layer can more accurately mirror face landmarks. In addition, many other methods have been adopted to locate face landmarks, namely, principal component analysis (PCA) [ 32 ], support vector machine [ 33 ], Bayesian probabilistic network (BPN) [ 34 ], dynamic link architecture (DLA) [ 35 ], and Gabor wavelet network (GWN) [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Local Privacy Protection for Sensitive Areas in Multiface Images

Liu

Yang

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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The privacy protection for face images aims to prevent attackers from accurately identifying target persons through face recognition. Inspired by goal-driven reasoning (reverse reasoning), this paper designs a goal-driven algorithm of local privacy protection for sensitive areas in multiface images (face areas) under the interactive framework of face recognition algorithm, regional growth, and differential privacy. The designed algorithm, named privacy protection for sensitive areas (PPSA), is realized in the following manner: Firstly, the multitask cascaded convolutional network (MTCNN) was adopted to recognize the region and landmark of each face. If the landmark overlaps a subgraph divided from the original image, the subgraph will be taken as the seed for regional growth in the face area, following the growth criterion of the fusion similarity measurement mechanism (FSMM). Different from single-face privacy protection, multiface privacy protection needs to deal with an unknown number of faces. Thus, the allocation of the privacy budget ε directly affects the operation effect of the PPSA algorithm. In our scheme, the total privacy budget ε is divided into two parts: ε_1 and ε_2. The former is evenly allocated to each seed, according to the estimated number of faces ρ contained in the image, while the latter is allocated to the other areas that may consume the privacy budget through dichotomization. Unlike the Laplacian (LAP) algorithm, the noise error of the PPSA algorithm will not change with the image size, for the privacy protection is limited to the face area. The results show that the PPSA algorithm meets the requirements ε-Differential privacy, and image classification is realized by using different image privacy protection algorithms in different human face databases. The verification results show that the accuracy of the PPSA algorithm is improved by at least 16.1%, the recall rate is improved by at least 2.3%, and F1-score is improved by at least 15.2%.

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Section: Introductionmentioning

confidence: 99%

Local Privacy Protection for Sensitive Areas in Multiface Images

Liu

Yang

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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“…With the rapid development of image processing technology, moving object detection technology in videos has been widely used. Especially in recent years, the application in sports, in which the video image processing technology, includes three parts of image acquisition, processing, and image secondary display [4,5]. Similarly, with the rapid development of the times, more and more video processing applications are proposed.…”

Section: Introductionmentioning

confidence: 99%

Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm

Hang-sheng

2021

Security and Communication Networks

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This paper studies the basketball goal recognition method based on image processing and improved algorithm to improve the accuracy of automatic recognition of basketball goal. The infrared spectrum image acquisition system is used to collect the basketball goal image. After the image is denoised by using the adaptive filtering algorithm, the wavelet analysis method is used to extract the features of basketball goal signal, which are input into the optimized deformable convolution neural network. Through the weighted sum of the values of each sampling point and the corresponding position authority of the block convolution core, the results are output as convolution operation. Combined with the depth feature of the same dimension, the full connection feature of the candidate target area is obtained to realize the basketball goal recognition. The experimental results show the following: the method can effectively identify basketball goals and the recognition error rate is low; the average accuracy of the automatic recognition results of basketball goals is as high as 98.4%; under the influence of different degrees of noise, the method is less affected by noise and has strong anti-interference ability.

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“…Inspired by visual selectivity, among the four parameters of the Gabor function, frequency and orientation have greater contributions and play the absolute role in the Gabor function. Perform Lipschitz [25][26][27][28][29][30]…”

Section: Improvement 33 Lipschitz-orthogonal-pruning-methods (Lopm Algorithm)mentioning

confidence: 99%

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

Yang¹,

Zhao²,

Su³

et al. 2021

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The conventional slow feature analysis (SFA) algorithm has no support of computational theory of vision for primates, nor does it have the ability to learn the global features with visual selection consistency continuity. And what is more, the algorithm is highly complex. Based on this, Slow Feature Extraction Algorithm Based on Visual selection consistency continuity and Its Application was proposed. Inspired by the visual selection consistency continuity theory for primates, this paper replaced the principal component analysis (PCA) method of the conventional SFA algorithm with the myTICA method, extracted the Gabor basis functions of natural images, initialized the basis function family; it used the feature basis expansion algorithm based on visual selection consistency continuity (the VSCC_FBEA algorithm) to replace the polynomial expansion method in the original SFA algorithm to generates the Gabor basis functions of features with long and short-term visual selectivity in the family of basis functions, which solved the drawbacks of the polynomial prediction algorithm; it also designed the Lipschitz consistency constraint, and proposed the Lipschitz-Orthogonal-Pruning-Method (LOPM algorithm) to optimize the basis function family into an over-complete family of basis functions. In addition, this paper used the feature expression method based on visual invariance theory (visual invariance theory -FEM) to establish the set of features of natural images with visual selection consistency continuity. Subsequently, it adopted three error evaluation methods and mySFA classification method to evaluate the proposed algorithm. According to the experimental results, the proposed algorithm showed good prediction performance with respect to the LSVRC2012 data set; compared with the SFA, GSFA, TICA, myICA and mySFA algorithms, the proposed algorithm is correct and feasible; when the classification threshold of the algorithm was set at 8.0, the recognition rate of the proposed algorithm reached 99.66%, and neither of the false recognition rate and the false rejection rate was higher than 0.33%. The proposed algorithm has good performance in prediction and classification, and also shows good anti-noise capacity under limited noise conditions.

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A Face Detection Method Based on Image Processing and Improved Adaptive Boosting Algorithm

Cited by 6 publications

References 0 publications

Local Privacy Protection for Sensitive Areas in Multiface Images

Local Privacy Protection for Sensitive Areas in Multiface Images

Research on Basketball Goal Recognition Based on Image Processing and Improved Algorithm

Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application

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