  Emotion recognition using digital image processing is a multifarious task
because facial emotions depend on warped facial features as well as on gender,
age, and culture. Furthermore, there are several factors such as varied
illumination and intricate settings that increase complexity in facial emotion
recognition. In this paper, we used four salient facial features, Eyebrows,
Mouth opening, Mouth corners, and Forehead wrinkles to identifying emotions
from normal, occluded and partially-occluded images. We have employed
rule-based approach and developed new methods to extract aforementioned facial
features similar to local bit patterns using novel techniques. We propose new
methods to detect eye location, eyebrow contraction, and mouth corners. For eye
detection, the proposed methods are Enhancement of Cr Red (ECrR) and
Suppression of Cr Blue (SCrB) which results in 98% accuracy. Additionally, for
eyebrow contraction detection, we propose two techniques (1) Morphological
Gradient Image Intensity (MGII) and (2) Degree of Curvature Line (DCL).
Additionally, we present a new method for mouth corners detection. For
classification purpose, we use an individual classifier, majority voting (MV)
and weighted majority voting (WMV) methods which mimic Human Emotions
Sensitivity (HES). These methods are straightforward to implement, improve the
accuracy of results, and work best for emotion recognition using partially
occluded images. It is ascertained from the results that our method outperforms
previous approaches. Overall accuracy rates are around 94%. The processing time
on one image using processor core i5 is ~0.12 sec.
