Encoding Local Binary Patterns using the re-parametrization of the second order Gaussian jet
In object recognition a robust feature set is considered as an important component in almost all the approaches proposed in the literature. In facial analysis, one of the best known feature set is based in Local Binary Patterns (LBP) which extracts the information contained in the image using comparisons between pixels in a region, finally such comparisons are encoded in form of histogram. We argue that this kind of encoding is statistically non-stable and can lead to errors during the recognition process, specially in noisy and low-resolution images, where the information contained in the image is not enough to generate a statistically robust histogram. In this paper, we propose a new method to encode the Local Binary Patterns using an re-parametrization of the second local order Gaussian Jet which generates more robust and reliable histograms suitable for different facial analysis tasks. We show that our method can be used for recognizing micro-expressions with competitive performances on the Spontaneous Micro-expression Corpus (SMIC) and the YORK Deception Detection Test.