In the field of computer vision, face detection concerns the positive identification of the faces of people within still images or video streams, which is extremely useful for applications such as counting, tracking, and recognition. When applied in large-scale environments, such as lecture theatres, we have found that existing technology can struggle greatly in detecting faces due primarily to the indiscernibility of their features, caused by partial occlusion, problematic orientation, and a lack of focus or resolution. We attempt to overcome this issue by proposing an adaptive framework, capable of collating the results of numerous existing detection systems in order to significantly improve recall rates. This approach uses supplementary modalities, invariant to the issues posed to features, to eliminate false detections from collated sets and allow us to produce results with extremely high confidence. The properties we have selected as the bases of detection classification are size and colour, as we believe that filters that consider them can be constructed adaptively, on a per-image basis, ensuring that the variabilities inherent to large-scale imagery can be fully accounted for, and that false detections and actual faces can be accurately distinguished between on a consistent basis. The application of principal component analysis to precise face detection results yields planar size distribution models that we can use to discard results that are either too large or too small to realistically represent faces within given images. Classifying a detection according to the correspondence of its general colour tone to the expected colour of skin is a more complex matter, however, as the apparent colour of skin is highly dependent upon incident illumination, and existing techniques are neither specific nor flexible enough to model it as accurately as we believe possible. Therefore, we propose another system, which will be able to adaptively model skin colour distributions according to the Gaussian probability densities exhibited by the colours of precise face detections. Furthermore, it will be suitable for independent application to real-time skin segmentation tasks as a result of considerable optimisation. This thesis details the design, the development, and the implementation of our systems, and thoroughly evaluates them with regards to the accuracy of their results and the efficiency of their performances, thereby establishing fully the suitability of them for solving certain types of presented problems.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:684781 |
| Date | January 2016 |
| Creators | Taylor, Michael James |
| Publisher | University of Manchester |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | https://www.research.manchester.ac.uk/portal/en/theses/adaptive-methodologies-for-realtime-skin-segmentation-and-largescale-face-detection(d20438dc-7b0a-4743-a6f6-76a59c87d741).html |
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