Abstract:
With an increasing emphasis on simulating human intelligence into machine intelligence, the future
demands advancement in the artificial intelligence technology. One of the major application areas in
machine vision is detection of objects. Image processing mainly addresses the issues of detection and
recognition of different prospects in an image. Due to random nature of images, it is not possible to
covereach and everything with 100% accuracy but an effort can be made by adding some fuzziness so
thatit works for almost everything.
Thisresearch presents a study and implementation of a skin detection system based on combination of
different color spaces using fuzzy logic. A simpler approach using boolean logic is also presented which
is, later on, enhanced using fuzzy logic. In the past, YCbCr and HSV have been extensively used for
skin detection but during our research the discrepancies (false alarms) showed the limitations of these
basic skin detectors. In order to reduce false alarms , three color spaces YCbCr, HSV and ROB have
been used to extract the general skin regions and the results of the three are combined using a fuzzy
inference system. Results reveal an accuracy of 97.30% for skin detection and a significant reduction in
therate of false alarms as compared to the boolean approach
