Housam Khalifa Bashier

Face recognition with Symmetric Local Graph Structure (SLGS)

Face recognition demonstrates the significant progress in the research field of biometric and computer vision. The fact is due to the current systems perform well under relatively control environments but tend to suffer when the present of variation in pose, illumination, and facial expression. In this work, a novel approach for face recognition called Symmetric Local Graph Structure (SLGS) is presented based on the Local Graph Structure (LGS). Each pixel is represented with a graph structure of its neighbours’ pixels. The histograms of the SLGS were used for recognition by using the nearest neighbour classifiers that include Euclidean distance, correlation coefficient and chi-square distance measures. AT&T and Yale face databases were used to be experimented with the proposed method. Extensive experiments on the face database clearly showed the superiority of the proposed approach over Local Binary Pattern (LBP) and LGS. The proposed SLGS is robust to variation in term of facial expressions, facial details, and illumination. Due to good performance of SLGS, it is expected that SLGS has a potential for application implementation in computer vision.

Face recognition using local graph theory (LGT)

The problem associated with Illumination variation is one of the major problems in image processing, pattern recognition, medical image, etc; hence there is a need to handle and deal with such variations. This paper presents a novel, simple, fast and efficient algorithm for face recognition call Local Graph Theory (LGT). LGT features are derived from a general definition of texture of graph neighborhood. The experiments results on our face database images demonstrated the effectiveness of the proposed method. The new method can be stabilized more quickly and obtain higher correct rate. Finally, LGT can be easily applied in many real time fields, such as image processing, pattern recognition, medical image as preprocessing.

Graphical password: Pass-images Edge detection

In this paper, we proposed and evaluated a new algorithm (EdgePass) to extract the edges of pass-images for a graphical password system. The EdgePass algorithm is based on an estimated technique for calculating the area in a plane bounded by a graph. This proposed scheme is the first stage of our graphical password system. The system framework is based on Edge detection, Image fusion and high spatial frequency component extraction. The idea of the proposed algorithm is to find whether the process pixel I(x, y) is dark or light. If it is light then the corresponding target pixel I(x, y) is an edge, else it is a background. The experiment results demonstrated the effectiveness of the proposed method. The new method can be stabilized more quickly.

Sequential graphical password framework for mobile devices

Nowadays, computer systems are very essential for human daily life for its ability to store and retrieve data in a more meaningful way. Thus, the data stored in computer system contains a very critical and important information and therefore, theres a critical needs to put more effort in handling of such data. Based on the traditional method, the most popular authentication way for computer security is using alphanumeric password. However, this type of authentication method come with few drawbacks. In recent years, many new methods have been developed by researchers to overcome the limitation of the password authentication, and one of the most popular method is known as graphical password, which utilizes picture as password instead of the alphanumeric string. Hence, this paper propose a new graphical password framework and method to be used in mobile-platform. Finally, the experiments results demonstrated the effectiveness of the proposed solution.