HOG-NPE: A Novel Local Description Operator for Face Image Recognition (Journal of Fiber Bioengineering and Informatics, 2014)
The method of extracting robust feature sets from an image is a crucial issue for the areas of computervision and pattern recognition. The nature of real data is a very high dimensional data. However, thehidden 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 visionand pattern recognition, etc. We employ the Histograms of Oriented Gradient (HOG) to extract therobust feature sets of facial images, which is a local description operator that possesses a certain degreeof 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 onthe local description operators. We use the novel local description operator - HOG-NPE for facial imagerecognition, and several experiments on well-known facial databases are conducted, which demonstrategood performance and effectiveness of this novel local description operator.
Keywords: Good Features; Histograms of Oriented Gradient; Subspace Learning; Neighborhood Preserving Embedding