A New Model to Detect 2D Hand based on Multi-feature Skin Model

Recognition of hand gesture is one of Human PCs most growing interfaces. In most vision-based signal recognition system, the initial phase is hand detection and separation. Because the hands are linked to a variety of day by day, local work experiences both extraordinary changes in the illumination and the innate unbroken appearance of the hand. In order to address these issues, we suggest another 2D hand position software that can be seen as a combination of multi-feature hand proposal generation and cascading neural system network characterization (CCNN). When considering various luminances we select color, Gabor, Hoard and Filter to separate the skin and produce a hand proposal. Therefore, we are selling a cascaded CNN that holds the deep setting information between the proposals. A mix of some datasets, including a few Oxford Hands Datasets, VIVA Hand Recognition, and Egohands Datasets, is tested as the positive example and image patch Net 2012, FDDEB dataset as a bad example; the proposed Multi-Feature Directed Cascaded CNN (MFS-CCNN) strategy. Aggressive results are achieved by the technique proposed. Our average sample dataset accuracy is considerably inferior to DPM. With an average of 43.55 and 51.78 percent accuracy, our CCNN and MFS-CCNN model perform DPM. Average accuracy of the CCNN model in a combined test set is 9.16% higher than the SSD model. Still, our model is faster than a DPM based on the statistical performance.

@article{Alotaibi2019,
  title     = {A New Model to Detect 2D Hand based on Multi-feature Skin Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {10},
  number    = {11},
  year      = {2019},
  publisher = {The Science and Information Organization},
  author    = {Abdullah Shawan Alotaibi},
  doi       = {10.14569/IJACSA.2019.0101110},
  url       = {https://doi.org/10.14569/IJACSA.2019.0101110}
}