  The Local Binary Patterns (LBP) is a local descriptor proposed by Ojala et al
to discriminate texture due to its discriminative power. However, the LBP is
sensitive to noise and illumination changes. Consequently, several extensions
to the LBP such as Median Binary Pattern (MBP) and methods such as Local
Directional Pattern (LDP) have been proposed to address its drawbacks. Though
studies by Zhou et al, suggest that the LDP exhibits poor performance in
presence of random noise. Recently, convolution neural networks (ConvNets) were
introduced which are increasingly becoming popular for feature extraction due
to their discriminative power. This study aimed at evaluating the sensitivity
of ResNet50, a ConvNet pre-trained model and local descriptors (LBP and LDP) to
noise using the Extended Yale B face dataset with 5 different levels of noise
added to the dataset. In our findings, it was observed that despite adding
different levels of noise to the dataset, ResNet50 proved to be more robust
than the local descriptors (LBP and LDP).
