Human Skin Colour Detection Algorithm Optimization with CUDA

This Human Skin Colour detection is analyzed to be a most suitable and strong cue for face detection and face recognition which finds various application in domain like surveillance, face biometrics, gesture recognition, interactive game application and many other. These applications require fast image processing in real time and demands enormously high performance with respect to time and processing speed. Earlier work done on sequential architecture does not provide required capability and can be achieved by parallel programming. As Image processing applications shows high degree of parallelism, they prove excellent source for multi-core platform. We can use GPU multithreaded parallel computing techniques to improve the speed of image processing. The paper proposes a data parallelism programming model for Human Skin Colour Detection algorithm. The objective is to increase the computational speed of the algorithm through data parallelism using CUDA framework. The framework is supported by OpenCV libraries and implemented by GPU (Graphics Processing Unit). The evaluation is done on basis of comparative analysis of serial and parallel programming computing. The pixel based Explicitly Defined Region skin classifier is used and RGB colour space is chosen due to its wider use in storing and processing of digital image data. To gain substantial acceleration in image computations, skin classifier code is off-loaded to GPU having compute capacity of 3.5 and rest of code is executed on CPU. We have tested module on static images, as well as on live camera captured images. SFA standard dataset is utilized to evaluate performance. Speedup of 23.45% was achieved using parallel programming.