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

Emotion recognition techniques with rule based and machine learning approaches