Age invariant face recognition system using automated voronoi diagram segmentation

One of the challenges in automatic face recognition is to achieve sequential face invariant. This is a challenging task because the human face undergoes many changes as a person grows older. In this study we will be focusing on age invariant features of a human face. The goal of this study is to investigate the face age invariant features that can be used for face matching, secondly is to come out with a prototype of matching scheme that is robust to the changes of facial aging and finally to evaluate the proposed prototype with the other similar prototype. The proposed approach is based on automated image segmentation using Voronoi Diagram (VD) and Delaunay Triangulations (DT). Later from the detected face region, the eyes will be detected using template matching together with DT. The outcomes, which are list of five coordinates, will be used to calculate interest distance in human faces. Later ratios between those distances are formulated. Difference vector will be use in the proposed method in order to perform face recognition steps. Datasets used for this research is selected images from FG-NET Aging Database and BioID Face Database, which is widely being used for image based face aging analysis; consist of 15 sample images taken from 5 different person. The selection is based on the project scopes and difference ages. The result shows that 11 images are successfully recognized. It shows an increase to 73.34% compared to other recent methods

Automatic detection of child pornography: A research in progress

Before the introduction of the internet, the availability of child pornography was reported as on the decline (Jenkins 2001). Since its emergence, however, the internet has made child pornography a much more accessible and available means of trafficking across borders (Biegel 2001; Jenkins 2001; Wells, Finkelhor et al. 2007). The internet as it is at present is made up of a vast array of protocols and networks where traffickers can anonymously share large volumes of illegal material amongst each other from locations with relaxed or non-existent laws that prohibit the possession or trafficking of illegal material. Likewise the internet is home to new developing social networks on the world wide web where young people are attracted to sharing their personal information amongst friends and family and inevitably become targets of predators. The volume and availability of such content, or targets for predators can be an overwhelming task for law enforcement to track and/or catalogue. In general cases image collections can range in the thousands (Taylor and Quayle 2003), and to assist in the identification and classification of child pornography within these large collections, the research of this author’s PhD seeks to establish a automated method of identifying and classifying material that has a high probability of being child pornography. This paper establishes the working progress of the author as they review existing relevant literature and hypothesise possible methods of identification and classificatio

Automatic detection of child pornography

Before the introduction of the internet, the availability of child pornography was reported as on the decline (Jenkins 2001). Since its emergence, however, the internet has made child pornography a much more accessible and available means of trafficking across borders (Biegel 2001; Jenkins 2001; Wells, Finkelhor et al. 2007). The internet as it is at present is made up of a vast array of protocols and networks where traffickers can anonymously share large volumes of illegal material amongst each other from locations with relaxed or non-existent laws that prohibit the possession or trafficking of illegal material. Likewise the internet is home to new developing social networks on the world wide web where young people are attracted to sharing their personal information amongst friends and family and inevitably become targets of predators. The volume and availability of such content, or targets for predators can be an overwhelming task for law enforcement to track and/or catalogue. In general cases image collections can range in the thousands (Taylor and Quayle 2003), and to assist in the identification and classification of child pornography within these large collections, the research of this author’s PhD seeks to establish a automated method of identifying and classifying material that has a high probability of being child pornography. This paper establishes the working progress of the author as they review existing relevant literature and hypothesise possible methods of identification and classification