The analysis of human facial expressions from the thermal images captured by the Infrared Thermal Imaging (IRTI) cameras has recently gained importance compared to images captured by the standard cameras using light having a wavelength in the visible spectrum. It is because infrared cameras work well in low-light conditions and also infrared spectrum captures thermal distribution that is very useful for building systems like Robot interaction systems, quantifying the cognitive responses from facial expressions, disease control, etc. In this paper, a deep learning model called IRFacExNet (InfraRed Facial Expression Network) has been proposed for facial expression recognition (FER) from infrared images. It utilizes two building blocks namely Residual unit and Transformation unit which extract dominant features from the input images specific to the expressions. The extracted features help to detect the emotion of the subjects in consideration accurately. The Snapshot ensemble technique is adopted with a Cosine annealing learning rate scheduler to improve the overall performance. The performance of the proposed model has been evaluated on a publicly available dataset, namely IRDatabase developed by RWTH Aachen University. The facial expressions present in the dataset are Fear, Anger, Contempt, Disgust, Happy, Neutral, Sad, and Surprise. The proposed model produces 88.43% recognition accuracy, better than some state-of-the-art methods considered here for comparison. Our model provides a robust framework for the detection of accurate expression in the absence of visible light.

This article presents a set of novel features for robust online Bangla handwritten character recognition. Two feature extraction methods are presented here. The first describes the transition from background to foreground pixels and vice versa. The second uses a combination of topological features and centre-of-gravity- (CG) based circular features where global information, local information, and Circular Quadrant Mass Distribution information have been extracted. The impact of each along with their combination have also been analyzed. A total of 15,000 isolated online Bangla character samples have been collected and used for the evaluation. A Support Vector Machine classifier records the best recognition rate when the transition count feature, CG-based circular features, and topological features are combined.

No abstract

No abstract

No abstract