People identification and tracking through fusion of facial and gait features

This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance

People identification and tracking through fusion of facial and gait features

This paper reviews the contemporary (face, gait, and fusion) computational approaches for automatic human identification at a distance. For remote identification, there may exist large intra-class variations that can affect the performance of face/gait systems substantially. First, we review the face recognition algorithms in light of factors, such as illumination, resolution, blur, occlusion, and pose. Then we introduce several popular gait feature templates, and the algorithms against factors such as shoe, carrying condition, camera view, walking surface, elapsed time, and clothing. The motivation of fusing face and gait, is that, gait is less sensitive to the factors that may affect face (e.g., low resolution, illumination, facial occlusion, etc.), while face is robust to the factors that may affect gait (walking surface, clothing, etc.). We review several most recent face and gait fusion methods with different strategies, and the significant performance gains suggest these two modality are complementary for human identification at a distance

Privacy-Preserving Authentication: A Homomorphic Encryption Approach

The importance of privacy for individuals has become increasingly evident in recent years as the amount of personal data being collected, stored and used by both private companies and government institutions has grown exponentially. The potential for this data to be misused or mishandled has led to widespread concern among individuals about the protection of their personal information. In response to these concerns, there has been a rise in the development of privacy-preserving technologies, which aim to protect personal data while still allowing it to be used for legitimate purposes. These technologies are necessary not only to address the concerns of individuals, but also to meet the legal requirements of institutions that handle personal information. Many applications using personal information as a commodity can benefit from privacy-preserving technologies. The research presented in this thesis targets a commonly used Internet application in which privacy-enhancing technologies can play a key role: biometric-based authentication. Authentication is the establishment of one party’s identity to the other. Biometric data, such as faces, fingerprints or iris, are used more and more commonly as a means of providing personal identification and authentication. However, authentication protocols using biometric data face serious privacy concerns, as the data involved is sensitive or personally-identifiable, which makes it necessary for data holders to protect its privacy. The widespread use of this application, and the need to protect user privacy, motivated us to examine how homomorphic encryption, a privacy-preserving technology, can be used and deployed to enhance privacy in such an application. Homomorphic encryption is a form of encryption that allows arbitrary computations to be performed on encrypted data, resulting in an encrypted result that, when decrypted, is the same as if the computation had been performed on the corresponding cleartext data. This means that entire computational processes can be executed on encrypted data without requiring the decryption key, thereby maintaining the privacy of the data involved. This can address both concerns from individuals regarding the protection of their personal and sensitive data, and legal requirements that institutions must meet. Homomorphic encryption can be used in an authentication protocol to allow a server to verify the authenticity of a client’s credentials without having access to the cleartext values of the credentials. In this thesis, we describe and prove secure two novel biometric-based authentication protocols that use homomorphic encryption to preserve the confidentiality of the biometric data both in storage and during use. These protocols ensure the privacy of the biometric information, while still allowing it to be used for authentication purposes. Users of the protocols encrypt their own biometric data and send it to a remote server that performs computations, including the biometric matching, solely on encrypted data. One of the protocols is designed to protect biometric data privacy against a honest-but-curious server and the other against a malicious server. Additionally, in both cases the user is securely authenticated by the server. For both the protocols, implementation and performance results using public homomorphic encryption libraries are presented along with a security and usability assessment, including an evaluation analysis against industry-standard biometric-based authentication schemes. In the most efficient implementation, the active authentication phase takes no more than three seconds to complete

Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року

Second International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2021). Kryvyi Rih, Ukraine, May 19-21, 2021.Друга міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні питання (ICSF 2021). Кривий Ріг, Україна, 19-21 травня 2021 року