Local Directional Maximum Edge Patterns for facial expression recognition

摘要

Cognitive science and neuroscience use human facial expressions of emotion. Every single facial expression can be seen at different passions in a face space. Nowadays, facial expression recognition and analysis is vital due to the demand of introducing advanced biometric applications in every domain space. The imperative task in facial expressions of emotion classification is precise feature extraction, which helps to get detailed description of facial marks. Existing feature descriptors are suffering from various problems such as intensity variations, discrimination, vulnerability etc. In this paper, propose a new feature descriptor method called LDMEP (Local Directional Maximum Edge Patterns) for facial expression analysis to overcome the hindrance. We calculated the gradients in four directions of reference pixel to elicit the more feature for better recognition instead of calculating the local differences among neighboring pixels. We also access the orientations of the pixels then thresholded based on the dynamic threshold to avoid the featureless area calculation. Furthermore, we considered only dominant magnitude and orientation directions instead of all eight directions to generate feature. Thus, imperative and efficient features are covered in dominant positions to detect the strong edges. The paper confers that how the subsequent model can be used for the recognition of facial expression of emotion.