Researchers have found that empathetic people interpret the facial expressions of dogs more intensely. The research suggests that humans interpret dog facial expressions in a similar way to human facial expressions. Empathy also makes people assess dogs' emotional facial expressions more rapidly. The research, based at the University of Helsinki and Aalto university, found that humans are good at recognizing the threatening facial expressions of dogs and considered them much more intense than similar threatening human expressions. However, humans rated happy facial expressions less intensely in dogs than they did in other humans.
Facial expression is a standout amongst the most imperative features of human emotion recognition. For demonstrating the emotional states facial expressions are utilized by the people. In any case, recognition of facial expressions has persisted a testing and intriguing issue with regards to PC vision. Recognizing the Micro-Facial expression in video sequence is the main objective of the proposed approach. For efficient recognition, the proposed method utilizes the optimal convolution neural network. Here the proposed method considering the input dataset is the CK+ dataset. At first, by means of Adaptive median filtering preprocessing is performed in the input image. From the preprocessed output, the extracted features are Geometric features, Histogram of Oriented Gradients features and Local binary pattern features. The novelty of the proposed method is, with the help of Modified Lion Optimization (MLO) algorithm, the optimal features are selected from the extracted features. In a shorter computational time, it has the benefits of rapidly focalizing and effectively acknowledging with the aim of getting an overall arrangement or idea. Finally, the recognition is done by Convolution Neural network (CNN). Then the performance of the proposed MFEOCNN method is analysed in terms of false measures and recognition accuracy. This kind of emotion recognition is mainly used in medicine, marketing, E-learning, entertainment, law and monitoring. From the simulation, we know that the proposed approach achieves maximum recognition accuracy of 99.2% with minimum Mean Absolute Error (MAE) value. These results are compared with the existing for MicroFacial Expression Based Deep-Rooted Learning (MFEDRL), Convolutional Neural Network with Lion Optimization (CNN+LO) and Convolutional Neural Network (CNN) without optimization. The simulation of the proposed method is done in the working platform of MATLAB.
An eerie robot with the face of a small child can make realistic-looking facial expressions. Creepy footage shows Affetto, an android with just a head and no body mimic human expressions like smiling and frowning. The robot was made by researchers from Osaka University in Japan who say it could open the door for androids to have'deeper interactions with humans'. Affetto, who has flesh-coloured skin on its face, can mimic a range of human expressions with incredible accuracy. An eerie robot with the face of a small child can make realistic-looking facial expressions.
The world's smallest bears copy one another's facial expressions as a means of communication. A team at the University of Portsmouth, UK, studied 22 sun bears at the Bornean Sun Bear Conservation Centre in Malaysia. In total, 21 matched the open-mouthed expressions of their playmates during face-to-face interactions. When they were facing each other, 13 bears made the expressions within 1 second of observing a similar expression from their playmate. "Mimicking the facial expressions of others in exact ways is one of the pillars of human communication," says Marina Davila-Ross, who was part of the team.