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Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human-computer interaction. Though a face displays different facial expressions, which can be immediately recognized by human eyes, it is very hard for a computer to extract and use the information content from these expressions. This paper proposes an approach for emotion recognition based on facial components. The local features are extracted in each frame using Gabor wavelets with selected scales and orientations. These features are passed on to an ensemble classifier for detecting the location of face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using the ensemble classifier. The eye and the mouth features are extracted using normalized semi-local binary patterns. The multiclass Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and CMU-MIT databases, and they exhibit significant performance improvement owing to their novel features when compared with the existing techniques.

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Facial occlusions like sunglasses, masks, caps etc. have severe consequences when reconstructing the partially occluded regions of a facial picture. This paper proposes a novel hybrid machine learning approach for occlusion removal based on Structural Similarity Index Measure (SSIM) and Principal Component Analysis (PCA), called SSIM_PCA. The proposed system comprises two stages. In the first stage, a Face Similar Matrix (FSM) guided by the Structural Similarity Index Measure is generated to provide the necessary information to recover from the lost regions of the face image. The FSM generates Related Face (RF) images similar to the probe image. In the second stage, these RF images are considered as related information and used as input data to generate eigenspaces using PCA to reconstruct the occluded face region exploiting the relationship between the occluded region and related face images, which contain relevant data to recover from the occluded area. Experimental results with three standard datasets viz. Caspeal-R1, IMFDB, and FEI have proven that the proposed method works well under illumination changes and occlusion of facial images.

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