- Title
- Age estimation and illicit image detection using a stochastic vision model
- Creator
- Islam, Mofakharul
- Date
- 2013
- Type
- Text; Thesis; PhD
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/44503
- Identifier
- vital:5314
- Abstract
- The main objective of this research is to investigate and implement a robust approach with a view to provide the Law Enforcement Agencies (LEAs) with a dedicated forensic tool in future for inspecting confiscated PCs from the suspected paedophile to detect pedophilic images automatically and prevent children viewing pornographic and age-inappropriate images at their home and school and adults at their workplace while they are on the Internet. To achieve this goal, we use a novel face descriptor to differentiate child face from adult face based on categorical age specific contextual cues that are based on new knowledge in terms of features or contexts representatives of child and adult face. Given that the craniofacial cues contain enough structural information on visual cues on human face encoded in the form of high level features we can categorize age into adult and children in tandem with low level features. Finally, we will present a novel stochastic vision model based on Markov Random Fields (MRF) prior, which learned the pornographic contextual constraints from the training pornographic images and eventually introduce knowledge on pornography into our proposed stochastic classifier allowing classification of images into pornographic or benign.; Doctor of Philosophy
- Publisher
- University of Ballarat
- Rights
- This metadata is freely available under a CCO license
- Subject
- Paedophile; Child; Pornography; MRF Modeling; Craniofacial; Stochastic; Classifier; Appearance; Contextual; Constraint
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