Rabia Jafri

Bio: Rabia Jafri is an academic researcher from King Saud University. The author has contributed to research in topics: Facial recognition system & Face detection. The author has an hindex of 11, co-authored 25 publications receiving 1002 citations. Previous affiliations of Rabia Jafri include University of Georgia.

A Survey of Face Recognition Techniques

Abstract: Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its many applications in various domains. Face recognition techniques can be broadly divided into three categories based on the face data acquisition methodology: methods that operate on intensity images; those that deal with video sequences; and those that require other sensory data such as 3D information or infra-red imagery. In this paper, an overview of some of the well-known methods in each of these categories is provided and some of the benefits and drawbacks of the schemes mentioned therein are examined. Furthermore, a discussion outlining the incentive for using face recognition, the applications of this technology, and some of the difficulties plaguing current systems with regard to this task has also been provided. This paper also mentions some of the most recent algorithms developed for this purpose and attempts to give an idea of the state of the art of face recognition technology.

Computer vision-based object recognition for the visually impaired in an indoors environment: a survey

Abstract: Though several electronic assistive devices have been developed for the visually impaired in the past few decades, however, relatively few solutions have been devised to aid them in recognizing generic objects in their environment, particularly indoors. Nevertheless, research in this area is gaining momentum. Among the various technologies being utilized for this purpose, computer vision based solutions are emerging as one of the most promising options mainly due to their affordability and accessibility. This paper provides an overview of the various technologies that have been developed in recent years to assist the visually impaired in recognizing generic objects in an indoors environment with a focus on approaches based on computer vision. It aims to introduce researchers to the latest trends in this area as well as to serve as a resource for developers who wish to incorporate such solutions into their own work.

Visual and Infrared Sensor Data-Based Obstacle Detection for the Visually Impaired Using the Google Project Tango Tablet Development Kit and the Unity Engine

Abstract: A novel visual and infrared sensor data-based system to assist visually impaired users in detecting obstacles in their path while independently navigating indoors is presented. The system has been developed for the recently introduced Google Project Tango Tablet Development Kit equipped with a powerful graphics processor and several sensors which allow it to track its motion and orientation in 3-D space in real-time. It exploits the inbuilt functionalities of the Unity engine in the Tango SDK to create a 3-D reconstruction of the surrounding environment, then associates a Unity collider component with the user and utilizes it to determine his interaction with the reconstructed mesh in order to detect obstacles. The user is warned about any detected obstacles via audio alerts. An extensive empirical evaluation of the obstacle detection component has yielded favorable results, thus, confirming the potential of this system for future development work.

Modeling and Simulation of Quality of Service for Composite Web Services

Abstract: As businesses begin to link Web services to create new functionality in the form of composite Web services, known as Web processes, it will become increasingly important to have a way of measuring their quality of service (QoS). To this end, we present a method that uses a predictive QoS model to compute the QoS for Web processes in terms of performance, cost and reliability. The ability to compute QoS for a Web process enables an organization to tune the process. Tuning Web processes presents an interesting problem. During the act of tuning, a business may want to explore many different configurations of the Web process in order to answer “what-if” questions. Composing and evaluating the QoS for many different configurations may be prohibitive in terms of time and costs. We present a simulation based technique to overcome this challenge to tuning Web processes.

Image Processing

Abstract: MUCKE aims to mine a large volume of images, to structure them conceptually and to use this conceptual structuring in order to improve large-scale image retrieval. The last decade witnessed important progress concerning low-level image representations. However, there are a number problems which need to be solved in order to unleash the full potential of image mining in applications. The central problem with low-level representations is the mismatch between them and the human interpretation of image content. This problem can be instantiated, for instance, by the incapability of existing descriptors to capture spatial relationships between the concepts represented or by their incapability to convey an explanation of why two images are similar in a content-based image retrieval framework. We start by assessing existing local descriptors for image classification and by proposing to use co-occurrence matrices to better capture spatial relationships in images. The main focus in MUCKE is on cleaning large scale Web image corpora and on proposing image representations which are closer to the human interpretation of images. Consequently, we introduce methods which tackle these two problems and compare results to state of the art methods. Note: some aspects of this deliverable are withheld at this time as they are pending review. Please contact the authors for a preview.

OpenFace: A general-purpose face recognition library with mobile applications

Abstract: Cameras are becoming ubiquitous in the Internet of Things (IoT) and can use face recognition technology to improve context. There is a large accuracy gap between today’s publicly available face recognition systems and the state-of-the-art private face recognition systems. This paper presents our OpenFace face recognition library that bridges this accuracy gap. We show that OpenFace provides near-human accuracy on the LFW benchmark and present a new classification benchmark for mobile scenarios. This paper is intended for non-experts interested in using OpenFace and provides a light introduction to the deep neural network techniques we use. We released OpenFace in October 2015 as an open source library under the Apache 2.0 license. It is available at: http://cmusatyalab.github.io/openface/ This research was supported by the National Science Foundation (NSF) under grant number CNS-1518865. Additional support was provided by Crown Castle, the Conklin Kistler family fund, Google, the Intel Corporation, and Vodafone. NVIDIA’s academic hardware grant provided the Tesla K40 GPU used in all of our experiments. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and should not be attributed to their employers or funding sources.

Face Recognition: A Literature Review

Abstract: The task of face recognition has been actively researched in recent years. This paper provides an up-to-date review of major human face recognition research. We first present an overview of face recognition and its applications. Then, a literature review of the most recent face recognition techniques is presented. Description and limitations of face databases which are used to test the performance of these face recognition algorithms are given. A brief summary of the face recognition vendor test (FRVT) 2002, a large scale evaluation of automatic face recognition technology, and its conclusions are also given. Finally, we give a summary of the research results. Keywords—Combined classifiers, face recognition, graph matching, neural networks.

Advanced authentication techniques and applications

Abstract: A system, apparatus, method, and machine readable medium are described for performing advanced authentication techniques and associated applications. For example, one embodiment of a method comprises: receiving a policy identifying a set of acceptable authentication capabilities; determining a set of client authentication capabilities; and filtering the set of acceptable authentication capabilities based on the determined set of client authentication capabilities to arrive at a filtered set of one or more authentication capabilities for authenticating a user of the client.