In this paper, we present "ThermoTrak", a smartphone accessory based, real-time and accurate temperature measurement mechanism, which can be used to screen for fever, which is a manifestation of infectious diseases including the symptoms caused by SARS-CoV-2. Our system accurately identifies face and forehead region from a safe distance of one meter, calculates accurate temperature of forehead with accuracy of ±0.5° C on a linear scale. An AI based algorithm is employed for the purpose of accurately detecting the Region of interest (ROI) (Face & point near center of Forehead) and calculate the absolute temperature within 300 milliseconds.

For synthesis of realistic facial expressions displaying emotions, we need an efficient representation of pure (e.g., surprise) as well as mixed (e.g., happily surprised) emotional expressions. In this paper, we train an expression map (XM) that efficiently represents the emotional expressions. We propose an algorithm that utilizes the XM to synthesize emotional expressions, tailor-made for the facial structure of the target person. The proposed method can also control the proportions of different basic emotional expressions of those, when mixed together, to generate realistic emotional facial expressions. Unlike many existing methods, our expression synthesis model requires only one expression-neutral face image of the target person. Both qualitative and quantitative tests on four data sets show promising results. On average, we have achieved 92.4% correct validation of the expressions synthesized by our method. We also show that for both basic and mixed emotional expressions, our method generates finer expression details compared to existing state-of-the-art works.

In this paper, a shape-constrained iterative algorithm is proposed to register a rigid template point-cloud to a given reference point-cloud. The algorithm embeds a shape-based similarity constraint into the principle of gravitation. The shape-constrained gravitation, as induced by the reference, controls the movement of the template such that at each iteration, the template better aligns with the reference in terms of shape. This constraint enables the alignment in difficult conditions introduced by change (presence of outliers and/or missing parts), translation, rotation and scaling. We discuss efficient implementation techniques with least manual intervention. The registration is shown to be important for change detection in the 3D point-cloud. The algorithm is compared with three state-of-the-art registration approaches. The experiments are done on both synthetic and real-world data. The proposed algorithm is shown to perform better in the presence of big rotation, structured and unstructured outliers and missing data.

MOTIVATION: Alzheimer's disease (AD) is a dementia that gets worse with time resulting in loss of memory and cognitive functions. The life expectancy of AD patients following diagnosis is ∼7 years. In 2006, researchers estimated that 0.40% of the world population (range 0.17-0.89%) was afflicted by AD, and that the prevalence rate would be tripled by 2050. Usually, examination of brain tissues is required for definite diagnosis of AD. So, it is crucial to diagnose AD at an early stage via some alternative methods. As the brain controls many functions via releasing signalling proteins through blood, we analyse blood plasma proteins for diagnosis of AD. RESULTS: Here, we use a radial basis function (RBF) network for feature selection called feature selection RBF network for selection of plasma proteins that can help diagnosis of AD. We have identified a set of plasma proteins, smaller in size than previous study, with comparable prediction accuracy. We have also analysed mild cognitive impairment (MCI) samples with our selected proteins. We have used neural networks and support vector machines as classifiers. The principle component analysis, Sammmon projection and heat-map of the selected proteins have been used to demonstrate the proteins' discriminating power for diagnosis of AD. We have also found a set of plasma signalling proteins that can distinguish incipient AD from MCI at an early stage. Literature survey strongly supports the AD diagnosis capability of the selected plasma proteins.