FAMC: Face Authentication for Mobile Concurrence

It has been observed in the last decades that face recognition has acquired a large amount of attention and curiosity. Benefits of this have been seen in quite a few applications. An architecture which has been implemented earlier addresses the face analysis domain. As compared to other biometrics, face recognition is more advantageous but it is particularly subject to spoofing. The whole cost of the system increases since the accuracy of this technique involves the estimation of the three dimensionality of faces. An effective and efficient solution for face spoofing has been proposed in the paper. The growing use of mobile devices has been a growing concern due to their ability to store and exchange sensitive data. Thus this has given encouragement to the interest of people, to exploit their abilities, from one side, and to protect users from malicious data, on the other side. It is important to develop and deliver secure access in this scenario and identification protocols on mobile platforms are another upcoming aspect that also requires attention to deal on the commercial and social use of identity management system. After all these conclusions, the earlier architecture proposes biometrics as the choice for technology which has been also implemented and described in the earlier architecture. The earlier architecture is designed for mobile devices. This architecture thus acts as an embedded application that provides both verification and identification functionality. It includes identity management to support social activities. Examples of identity management system are finding doubles in a social network. Privacy has been provided by these functionalities which help to overcome the security concern. The architecture of FAMC: Face Authentication for Mobile Concurrence is modular. Functionalities like image acquisition, anti-spoofing, face detection, face segmentation; feature extraction and face matching have been provided by its implementation. The behavior of FAMC allows for recognition and best biometrics sample selection. DOI: 10.17762/ijritcc2321-8169.150310

Prescribing Challenges after Bariatric Surgery

Obesity is an increasing problem in the UK, with over half the population being overweight or obese. The use of gastric surgery is increasing, with a 5% increase in 2016/17 compared to 2015/16. However, little is known about ideal drug formulations after bariatric surgery. An exploratory literature search of research databases was carried out to address this. We found that there was a dearth of high-quality primary studies available, with many studies using low numbers of participants. The major finding was of the need for increased vigilance and monitoring of patients after surgery

Spoofing Attacks To 2D Face Recognition Systems With 3D Masks

Vulnerability to spoofing attacks is a serious drawback for many biometric systems. Among all biometric traits, face is the one that is exposed to the most serious threat, since it is exceptionally easy to access. The limited work on fraud detection capabilities for face mainly shapes around 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices. A significant portion of this work is based on the flatness of the facial surface in front of the sensor. In this study, we complicate the spoofing problem further by introducing the 3rd dimension and ex- amine possible 3D attack instruments. A small database is constructed with six different types of 3D facial masks and it is utilized to conduct experiments on state-of-the-art 2D face recognition systems. Spoofing performance for each type of mask is assessed and analysed thoroughly

Spoofing Face Recognition with 3D Masks

Spoofing is the act of masquerading as a valid user by falsifying data to gain an illegitimate access. Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in biometrics domain and among all biometric traits, face is exposed to the most serious threat, since it is particularly easy to access and reproduce. In the literature, many different types of face spoofing attacks have been examined and various algorithms have been proposed to detect them. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. However, with the advancements in 3D reconstruction and printing technologies, this assumption can no longer be maintained. In this paper, we aim to inspect the spoofing potential of subject-specific 3D facial masks for different recognition systems and address the detection problem of this more complex attack type. In order to assess the spoofing performance of 3D masks against 2D, 2.5D and 3D face recognition and to analyse various texture based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two datasets: The Morpho database which is not publicly available and the newly distributed 3D mask attack database