MAFONN-EP: A Minimal Angular Feature Oriented Neural Network based Emotion Prediction system in image processing

Autor: L.B. Krithika, G.G. Lakshmi Priya
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Journal of King Saud University: Computer and Information Sciences, Vol 34, Iss 1, Pp 1320-1329 (2022)
Druh dokumentu: article
ISSN: 1319-1578
DOI: 10.1016/j.jksuci.2018.09.011
Popis: In recent days, facial emotion recognition techniques are considered important and critical due wide array of application domains use facial emotion as part of their workflow and analytics gathering. Reduced recognition rate, inefficient computation and increased time consumption are the major drawbacks of these techniques. To overcome these issues, this paper develops the new facial emotion recognition technique named as Minimal Angular Feature Oriented Neural Network based Emotion Prediction (MAFONN-EP). Initially, the input video sequence are categorized into image frames that are pre-processed by eliminating the noise by using Weighted Median Filtering (WMF) technique and to separate the background and foreground regions using Edge Preserved Background Separation and Foreground Extraction (EPBSFE) technique. Then, the set of texture patterns are extracted based on four key parts such as two eyes, nose and mouth by using the Minimal Angular Deviation (MAD) technique. Particular features are selected by employing the Cuckoo Search based Particle Swarm Optimization (CS-PSO) technique that also reduces the feature dimensionality. Finally, the Weight Based Pointing Kernel Classification (WBPKC) technique is employed for recognizing the emotion. In the experimental results, the performance of the proposed technique is analyzed and compared with different performance measures like accuracy, sensitivity, specificity, precision, recall.
Databáze: Directory of Open Access Journals
Externí odkaz: