TY - JOUR T1 - HOG-NPE: A Novel Local Description Operator for Face Image Recognition JO - Journal of Fiber Bioengineering and Informatics VL - 3 SP - 419 EP - 427 PY - 2014 DA - 2014/07 SN - 7 DO - http://doi.org/10.3993/jfbi09201411 UR - https://global-sci.org/intro/article_detail/jfbi/4797.html KW - Good Features KW - Histograms of Oriented Gradient KW - Subspace Learning KW - Neighborhood Preserving Embedding AB - The method of extracting robust feature sets from an image is a crucial issue for the areas of computer vision and pattern recognition. The nature of real data is a very high dimensional data. However, the hidden structure can be well characterized by a small number of features in most cases. As a result, the method of extracting a small number of good features is an important question in computer vision and pattern recognition, etc. We employ the Histograms of Oriented Gradient (HOG) to extract the robust feature sets of facial images, which is a local description operator that possesses a certain degree of invariance against geometric and photometric deformations. Neighborhood Preserving Embedding (NPE) which is a subspace learning algorithm is adopted to extract a small number of good features on the local description operators. We use the novel local description operator - HOG-NPE for facial image recognition, and several experiments on well-known facial databases are conducted, which demonstrate good performance and effectiveness of this novel local description operator.

Hongfeng Wang. (2019). HOG-NPE: A Novel Local Description Operator for Face Image Recognition. Journal of Fiber Bioengineering and Informatics. 7 (3). 419-427. doi:10.3993/jfbi09201411