Evaluation of LBP-Based Facial Emotions Recognition Techniques to Make Consistent Decisions

摘要

Decision making is one of the smouldering problems in day to day works. Human emotions play crucial role in decision-making systems. While person is in high emotion he cannot make proper decision. Robust local binary pattern (RLBP) operator is more powerful to recognize the emotions and extends the features of local binary pattern (LBP). However, there are some precincts like discriminating bright faces against dark features and vice versa and intra-class variances increase. The RLBP solves this problem by finding minimum of LBP codes and their complements. However, it miss the mark for different local structures a similar feature is obtained, weak contrast local patterns and similar strong contrast local patterns. Hence, the discriminative robust local binary pattern (DRLBP) method is proposed to retain the contrast information of image patterns next to considering both edge and texture information. Nevertheless, LBP family methods are highly sensitive to noise. To trounce these drawbacks this paper extends fuzzy rule-based DRLBP which is more robust to noise, low contrasted, uneven lighting conditions, variations in expressions and rotation variant images.