Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

Automatic facial expression recognition is widely used for various applications such as health care, surveillance and human-robot interaction. In this paper, we present a novel system which employs automatic facial emotion recognition technique for adaptive AI agent behaviour. The proposed system is equipped with kirsch operator based local binary patterns for feature extraction and diverse classifiers for emotion recognition. First, we nominate a novel variant of the local binary pattern (LBP) for feature extraction to deal with illumination changes, scaling and rotation variations. The features extracted are then used as input to the classifier for recognizing seven emotions. The detected emotion is then used to enhance the behaviour selection of the artificial intelligence (AI) agents in a shooter game. The proposed system is evaluated with multiple facial expression datasets and outperformed other state-of-the-art models by a significant margin

Multi-Population Differential Evolution for Retinal Blood Vessel Segmentation

The retinal blood vessel segmentation plays a significant role in the automatic or computer-assisted diagnosis of retinopathy. Manual blood vessel segmentation is very time-consuming and requires a great amount of domain knowledge. In addition, the blood vessels are only a few pixels wide and cover the entire fundus image. This further hinders the recent systems from automating the retinal blood vessel segmentation efficiently. In this paper, we propose a modified differential evolution (DE) algorithm to carry out automatic retinal blood vessel segmentation. The modified DE employs cross-communication among multiple populations to select three types of features i.e. thick blood vessels, thin blood vessels and non-blood vessels. Multiple classifiers such as neural networks (NN), Support vector machines (SVM), NN based and SVM based ensembles are used to further measure the performance of segmentation. The proposed algorithm is evaluated on three publicly available retinal image datasets like DRIVE, STARE and HRF. It outperformed the state-of-the-art with a high average accuracy of 98.5% along with high sensitivity and specificity

Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour

This publication may contain explicit sexual literary descriptions and/or artistic depictions