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Soft biometrics can be used as a prescreening filter, either by using single trait or by combining several traits to aid the performance of recognition systems in an unobtrusive way. In many practical visual surveillance scenarios, facial information becomes difficult to be effectively constructed due to several varying challenges. However, from distance the visual appearance of an object can be efficiently inferred, thereby providing the possibility of estimating body related information. This paper presents an approach for estimating body related soft biometrics; specifically we propose a new approach based on body measurement and artificial neural network for predicting body weight of subjects and incorporate the existing technique on single view metrology for height estimation in videos with low frame rate. Our evaluation on 1120 frame sets of 80 subjects from a newly compiled dataset shows that the mentioned soft biometric information of human subjects can be adequately predicted from set of frames.

Nowadays, Green IoT-Based Agriculture plays an essential role in farming to improve the yield. Here, IoT devices are embedded in the farming equipment, which helps to enhance the irrigation and yield with minimum cost-cutting. Data security and privacy are major challenges in green IoT-related agriculture. Therefore, a secured system should create to maintain data confidentiality, authentication, integrity, availability, and privacy. This system uses the privacy-preserving data aggregation (PPDA) with a Fair access framework (FAF) that manages the data security. The data aggregation concept is used to protect the green IoT data from false data injection. The FAF utilizes the blockchain technique to grant, get, revoke and delegate access to the user. The developed security system can adapt the green IoT-based agriculture and provide confidentiality, which is done with the help of an enhanced ciphertext access control mechanism. This system resolves the security and privacy issues involved in the Green IoT-based agriculture, and the effectiveness of the system is evaluated using implementation results. [ABSTRACT FROM AUTHOR]

Face as a biometric attribute has been extensively studied over the past few decades. Even though, satisfactory results are already achieved in controlled environments, the practicality of face recognition in realistic scenarios is still limited by several challenges, such as, expression, pose, occlusion, etc. Recently, the research direction is concentrating on the prospects of complementing face recognition systems with facial soft biometric traits. The ease of extracting facial soft biometrics under several varying conditions has mainly resulted in the ability of using the traits to, either improve the performance of traditional face recognition systems, or performing recognition solely based on many facial soft biometrics. This paper presents state-of-the-art techniques in facial soft biometrics research by describing the type of traits, feature extraction methods, and the application domains. It indicates the most recent and valuable results attained, while also highlighting some possible future scientific research directions to be investigated. [ABSTRACT FROM AUTHOR]

Gender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple samples per individual to improve the learning ability of the classifier, or learning gender based on prior knowledge about pose and demographic distributions of the dataset. However, image alignment increases the complexity and time of computation, while the use of multiple samples or having prior knowledge about data distribution is unrealistic in practical applications. This paper presents an approach for gender recognition from unconstrained face images. Our technique exploits the robustness of local feature descriptor to photometric variations to extract the shape description of the 2D face image using a single sample image per individual. The results obtained from experiments on Labeled Faces in the Wild (LFW) dataset describe the effectiveness of the proposed method. The essence of this study is to investigate the most suitable functions and parameter settings for recognizing gender from unconstrained face images. [ABSTRACT FROM AUTHOR]

Handwritten signature biometric is considered as a noninvasive and nonintrusive process by the majority of the users. Furthermore, it has a high legal value for document authentication, as well as being dependent on by both commercial transactions and governmental institutions. Signature verification requires storing templates in the database, which threatens the security of the system from being stolen, to being vulnerable to the template playback attack that may give an attacker an invalid access to the system. Moreover, an individual cannot use his / her signature with two applications or more, otherwise, a cross matching problem will occur. The aforementioned problems can be avoided by using biometric template protection techniques for the online signature, which have been reviewed and discussed in this paper considering both protections and verification. Furthermore, the verification elaboration comprises of capture devices, pre-processing, feature extraction and classification methods. [ABSTRACT FROM AUTHOR]

We propose a computational approach to human identification based on the integration of face and body related soft biometric traits. In previous studies on soft biometrics, several methods for human identification using semantic descriptions have been introduced. Though the results attained exhibit the effectiveness of such techniques in image retrieval and short term tracking of subjects, semantics literally limits the ability of a biometric system to provide conclusive identification. This paper presents a new framework for biometric identification based solely on multiple measured soft biometric traits. The paper describes techniques for extracting/estimating face and body based soft biometric traits from frame set. Furthermore, we utilized a sequential attribute combination method to perform attribute selection prior to integration at match score level. Finally, an evaluation of five score fusion techniques is performed. The results show that the proposed framework can be utilized to model an adequate soft biometric system with rank-1 identification rate of 88%. [ABSTRACT FROM AUTHOR]