A Street View Image Privacy Detection and Protection method based on Mask-RCNN

Yu, Jinao; Wu, Min; Li, Changqing; Zhu, Shibing

doi:10.1109/itaic49862.2020.9338847

Cited by 9 publications

(7 citation statements)

References 6 publications

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“…Mask R-CNN [12] is a method that uses instance-aware semantic segmentation to label segmentation or classify objects, so that this method can distinguish objects/instances in the same class. Mask R-CNN are usually used for detection [14], [50] and object recognition [14], [51]. Mask R-CNN extends faster R-CNN by adding a new branch to predict the mask object in parallel with the existing branch for bounding box recognition.…”

Section: Selection Stagementioning

confidence: 99%

“…This study will use the mask R-CNN method, this method was chosen because this method is proven to have good performance in object detection and recognition [4], [12], [14], in addition to therefore, this method can distinguish the same object in one image with instance-aware semantic segmentation or fully convolutional network (FCN). For example, in one image there are many objects in the form of cars, using this method we can identify an instance of each car object in the image at the pixel level.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Budiarsa,

Wardoyo,

Musdholifah

2023

IJECE

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Face recognition technology has been used in many ways, such as in the authentication and identification process. The object raised is a piece of face image that does not have complete facial information (occluded face), it can be due to acquisition from a different point of view or shooting a face from a different angle. This object was raised because the object can affect the detection and identification performance of the face image as a whole. Deep leaning method can be used to solve face recognition problems. In previous research, more focused on face detection and recognition based on resolution, and detection of face. Mask region convolutional neural network (mask R-CNN) method still has deficiency in the segmentation section which results in a decrease in the accuracy of face identification with incomplete face information objects. The segmentation used in mask R-CNN is fully convolutional network (FCN). In this research, exploration and modification of many FCN parameters will be carried out using the CNN backbone pooling layer, and modification of mask R-CNN for face identification, besides that, modifications will be made to the bounding box regressor. it is expected that the modification results can provide the best recommendations based on accuracy.

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Section: Selection Stagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Budiarsa,

Wardoyo,

Musdholifah

2023

IJECE

View full text Add to dashboard Cite

show abstract

“…Lastly, transitioning from the methods of face deidentification to a method based on Mask-RCNN, a research study by Yu et al [45] focuses on the critical issue of preserving privacy within street view images, which often contain sensitive data. Traditionally, privacy protection involved manual identification and obfuscation of sensitive content.…”

Section: Face De-identificationmentioning

confidence: 99%

Preserving Privacy in Video Analytics: A Comprehensive Review of Face De-Identification and Background Blurring Techniques

Maity,

More,

Kambli

et al. 2023

Preprint

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The landscape of privacy protection within the domain of video analytics is in a constant state of evolution, driven by advances in computer vision and machine learning techniques. These advancements provide increasingly sophisticated video analysis capabilities, but they also raise the stakes in terms of personal privacy. In this review, we have conducted a thorough exploration into the intersection of video analytics and privacy preservation, focusing on two core techniques: face de-identification and background blurring. We rigorously analyze the latest advancements in face de-identification, including the nuanced approaches involving facial feature perturbation, deep learning methodologies, and the intriguing domain of generative adversarial networks, meticulously highlighting both their efficacy and inherent limitations. Simultaneously, we research numerous background blurring techniques, showcasing their remarkable ability to obscure contextual information while preserving the essential elements of the visual scene. This paper underscores the practical significance of these techniques through real-world applications in surveillance and the dynamic landscape of social platforms. In essence, our comprehensive review combines the multifaceted aspects of face de-identification and background blurring within the sphere of video analytics, thereby offering a nuanced and thorough understanding of this pivotal domain.

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“…Admittedly, audiovisual methods are quite economical and easy-achieved, driven by the popularization of mobiles today. But human faces and voices are especially privacy-sensitive [28][29][30]. There are a significant proportion of people who resist uploading such information to avoid privacy leakage.…”

Section: Introductionmentioning

confidence: 99%

Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition

Qi,

Jia,

Feng

et al. 2024

IEEE Trans. Comput. Soc. Syst.

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Emotion recognition, a process of automatic cognition of human emotions, has great potential to improve the degree of social intelligence. Among various recognition methods, Emotion recognition based on touch event's temporal and force information receives global interests. Although previous studies have shown promise in the field of keystroke-based emotion recognition, they are limited by the need for long-term text input and the lack of high-precision force sensing technology, hindering their real-time performance and wider applicability. To address this issue, in this paper, a piezoelectric-based keystroke dynamic technique is presented for quick emotion detection. The nature of piezoelectric materials enables high-resolution force detection. Meanwhile, the data collecting procedure is highly simplified because only the password entry is needed. International Affective Digitized Sounds (IADS) are applied to elicit users ' emotions, and a PAD emotion scale is used to evaluate and label the degree of emotion induction. A Random Forest (RF) based algorithm is used in order to reduce the training dataset and improve algorithm portability. Finally, an average recognition accuracy of 79.37% of 4 emotions (happiness, sadness, fear, disgust) is experimentally achieved. The proposed technique improves the reliability and practicability of emotion recognition in realistic social systems.

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A Street View Image Privacy Detection and Protection method based on Mask-RCNN

Cited by 9 publications

References 6 publications

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Preserving Privacy in Video Analytics: A Comprehensive Review of Face De-Identification and Background Blurring Techniques

Piezoelectric Touch Sensing and Random-Forest-Based Technique for Emotion Recognition

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