A Machine Learning Approach for Expression Detection in Healthcare Monitoring Systems  

作　　者：Muhammad Kashif Ayyaz Hussain Asim Munir Abdul Basit Siddiqui AaqifAfzaal Abbasi Muhammad Aakif Arif Jamal Malik Fayez Eid Alazemi Oh-Young Song 

机构地区：[1]Department of Computer Science&Software Engineering,International Islamic University,Islamabad,44000,Pakistan [2]Department of Computer Science,Quaid-i-Azam University,Islamabad,44000,Pakistan [3]Department of Computer Science,Capital University of Science&Technology,Islamabad,44000,Pakistan [4]Department of Software Engineering,Foundation University,Islamabad,44000,Pakistan [5]Department of Computer Science,Abdul Wali Khan University,Mardan,23200,Pakistan [6]Department of Computer Science and Information Systems,College of Business Studies,PAAET,12062,Kuwait [7]Department of Software,Sejong University,Seoul,05006,Korea

出　　处：《Computers, Materials & Continua》2021年第5期2123-2139,共17页计算机、材料和连续体（英文）

基　　金：supported in part by the MSIT(Ministry of Science and ICT),Korea,under the ITRC(Information Technology Research Center)support program(IITP2020-2016-0-00312);supervised by the IITP(Institute for Information&communications Technology Planning&Evaluation)and in part by the Faculty Research Fund of Sejong University in 2019.

摘　　要：Expression detection plays a vital role to determine the patient’s condition in healthcare systems.It helps the monitoring teams to respond swiftly in case of emergency.Due to the lack of suitable methods,results are often compromised in an unconstrained environment because of pose,scale,occlusion and illumination variations in the image of the face of the patient.A novel patch-based multiple local binary patterns(LBP)feature extraction technique is proposed for analyzing human behavior using facial expression recognition.It consists of three-patch[TPLBP]and four-patch LBPs[FPLBP]based feature engineering respectively.Image representation is encoded from local patch statistics using these descriptors.TPLBP and FPLBP capture information that is encoded to find likenesses between adjacent patches of pixels by using short bit strings contrary to pixel-based methods.Coded images are transformed into the frequency domain using a discrete cosine transform(DCT).Most discriminant features extracted from coded DCT images are combined to generate a feature vector.Support vector machine(SVM),k-nearest neighbor(KNN),and Naïve Bayes(NB)are used for the classification of facial expressions using selected features.Extensive experimentation is performed to analyze human behavior by considering standard extended Cohn Kanade(CK+)and Oulu–CASIA datasets.Results demonstrate that the proposed methodology outperforms the other techniques used for comparison.

关 键 词：Detection EXPRESSIONS GESTURES ANALYTICS PAIN patch-based local binary descriptor discrete cosine transform healthcare 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]