Li Siying

Bio: Li Siying is an academic researcher from Beijing University of Posts and Telecommunications. The author has contributed to research in topics: Feature extraction & Matrix (mathematics). The author has an hindex of 1, co-authored 3 publications receiving 14 citations.

A Review of Face Recognition Technology

Abstract: Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. People collect the face images, and the recognition equipment automatically processes the images. The paper introduces the related researches of face recognition from different perspectives. The paper describes the development stages and the related technologies of face recognition. We introduce the research of face recognition for real conditions, and we introduce the general evaluation standards and the general databases of face recognition. We give a forward-looking view of face recognition. Face recognition has become the future development direction and has many potential application prospects.

Image classification method and device, electronic equipment and readable storage medium

Abstract: The embodiment of the invention provides an image classification method and device, electronic equipment and a readable storage medium, which are applied to the technical field of pattern recognition,and the method comprises the following steps: carrying out graying processing on each sample image, and representing each obtained gray image as a two-dimensional image matrix, wherein the sample image comprises a training image and an image to be classified; establishing a gray matrix according to each two-dimensional image matrix; according to the label constraint matrix and a non-negative matrix factorization algorithm based on a half tensor product, performing feature extraction on the normalized gray matrix to obtain a feature matrix; transposing the product of the label constraint matrix and the feature matrix as a mixed feature matrix; and inputting the features corresponding to the images to be classified in the mixed feature matrix into a pre-established image classification model to obtain a classification result, the image classification model being obtained by training the features corresponding to the training images in the mixed feature matrix and the image categories corresponding to the training images. The image classification efficiency is improved.

Image encryption method and device and electronic equipment

Abstract: Embodiments of the invention provide an image encryption method and device and electronic equipment. The method comprises the steps of determining a carrier image and a to-be-encrypted image; performing discrete wavelet transform on the carrier image to obtain a first discrete wavelet map; performing discrete wavelet transform on the to-be-encrypted image to obtain a second discrete wavelet map; splicing the first discrete wavelet map and the second discrete wavelet map to obtain a spliced wavelet map; and encrypting the spliced wavelet map by using a chaotic compressed sensing algorithm to obtain an encrypted image. In the embodiment of the invention, the method comprises the following steps of: obtaining the data, even if an encryption secret key is used in a chaotic compressed sensing encryption algorithm; the chaotic matrix generation system is stolen by an attacker; an attacker carries out chaotic compression and decryption and carries out inverse discrete wavelet transformation on a discrete wavelet map obtained through decryption to obtain a visually meaningful hidden image, so that the probability that the attacker further cracks an encrypted image is reduced, the attackercannot obtain the encrypted image, and the security of image encryption is improved.

Hybrid Transfer Learning and Broad Learning System for Wearing Mask Detection in the COVID-19 Era

Abstract: In the era of Corona Virus Disease 2019 (COVID-19), wearing a mask can effectively protect people from infection risk and largely decrease the spread in public places, such as hospitals and airports. This brings a demand for the monitoring instruments that are required to detect people who are wearing masks. However, this is not the objective of existing face detection algorithms. In this article, we propose a two-stage approach to detect wearing masks using hybrid machine learning techniques. The first stage is designed to detect candidate wearing mask regions as many as possible, which is based on the transfer model of Faster_RCNN and InceptionV2 structure, while the second stage is designed to verify the real facial masks using a broad learning system. It is implemented by training a two-class model. Moreover, this article proposes a data set for wearing mask detection (WMD) that includes 7804 realistic images. The data set has 26403 wearing masks and covers multiple scenes, which is available at “ https://github.com/BingshuCV/WMD .” Experiments conducted on the data set demonstrate that the proposed approach achieves an overall accuracy of 97.32% for simple scene and an overall accuracy of 91.13% for the complex scene, outperforming the compared methods.

Online Student Authentication and Proctoring System Based on Multimodal Biometrics Technology

Abstract: Identity verification and proctoring of online students are one of the key challenges to online learning today. Especially for online certification and accreditation, the training organizations need to verify that the online students who completed the learning process and received the academic credits are those who registered for the courses. Furthermore, they need to ensure that these students complete all the activities of online training without cheating or inappropriate behaviours. The COVID-19 pandemic has accelerated (abruptly in certain cases) the migration and implementation of online education strategies and consequently the need for safe mechanisms to authenticate and proctor online students. Nowadays, there are several technologies with different grades of automation. In this paper, we deeply describe a specific solution based on the authentication of different biometric technologies and an automatic proctoring system (system workflow as well as AI algorithms), which incorporates features to solve the main concerns in the market: highly scalable, automatic, affordable, with few hardware and software requirements for the user, reliable and passive for the student. Finally, the technological performance test of the large scale system, the usability-privacy perception survey of the user and their results are discussed in this work.

A Novel Approach to Face Pattern Analysis

Abstract: Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real-time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, PCA with an Artificial Neural Network, and even the traditional PCA-SVM to improve face recognition. PCA-SVM is better than PCA-ANN as PCA-ANN has the limitation of a small dataset. As far as classification and generalization are concerned, SVM requires fewer parameters and generates less generalization errors than an ANN. In this paper, we propose a new framework, called FRS-DCT-SVM, that uses GA-RBF for face detection and optimization and the discrete cosine transform (DCT) to extract features. FRS-DCT-SVM using GA-RBF gives better results in terms of clustering time. The average accuracy received by FRS-DCT-SVM using GA-RBF is 98.346, which is better than that of PCA-SVM and SVM-DCT (86.668 and 96.098, respectively). In addition, a comparison is made based on the training, testing, and classification times.

Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects

Abstract: The present information age is characterized by an ever-increasing digitalization. Smart devices quantify our entire lives. These collected data provide the foundation for data-driven services called smart services. They are able to adapt to a given context and thus tailor their functionalities to the user’s needs. It is therefore not surprising that their main resource, namely data, is nowadays a valuable commodity that can also be traded. However, this trend does not only have positive sides, as the gathered data reveal a lot of information about various data subjects. To prevent uncontrolled insights into private or confidential matters, data protection laws restrict the processing of sensitive data. One key factor in this regard is user-friendly privacy mechanisms. In this paper, we therefore assess current state-of-the-art privacy mechanisms. To this end, we initially identify forms of data processing applied by smart services. We then discuss privacy mechanisms suited for these use cases. Our findings reveal that current state-of-the-art privacy mechanisms provide good protection in principle, but there is no compelling one-size-fits-all privacy approach. This leads to further questions regarding the practicality of these mechanisms, which we present in the form of seven thought-provoking propositions.