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Covid-19 a small virus has created a havoc in the world. The pandemic has already taken over 4 lakh lives. The tests to detect a Covid-19 positive takes time and is costly. Moreover, the ability of the virus to mutate surprises the doctors every day. Present paper proposes a saliency-based model called Deep_Saliency. The model works on chest x-rays of healthy, unhealthy, and covid-19 patients. An x-ray repository of Covid-19, available in public domain, is taken for the study. Deep_Saliency uses visual, disparity, and motion saliency to create a feature dataset of the x-rays. The collected features are tested and trained using Long Short-Term Memory (LSTM) network. A predictive analysis is performed using the x-ray of a new patient to confirm a Covid-19 positive case. The first objective of the paper is to detect Covid-19 positive cases from x-rays. The other objective is to provide a benchmark dataset of biomarkers. The proposed work achieved an accuracy of 96.66%.

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Facial expression plays an important role in communicating emotions. In this paper, a robust method for recognizing facial expressions is proposed using the combination of appearance features. Traditionally, appearance features mainly divide any face image into regular matrices for the computation of facial expression recognition. However, in this paper, we have computed appearance features in specific regions by extracting facial components such as eyes, nose, mouth, and forehead, etc. The proposed approach mainly has five stages to detect facial expression viz. face detection and regions of interest extraction, feature extraction, pattern analysis using a local descriptor, the fusion of appearance features and finally classification using a Multiclass Support Vector Machine (MSVM). Results of the proposed method are compared with the earlier holistic representations for recognizing facial expressions, and it is found that the proposed method outperforms state-of-the-art methods.