Pub Date : 2020-04-01DOI: 10.1109/CSASE48920.2020.9142085
N. F. Hasan, S. Q. Mahdi
Research on age estimation witnessed increasing attention due to the demand for its applications. The age estimation has an essential role in preventing under-age persons from performing adult activities. The proposed age estimation technique is carried out through several stages; preprocessing, feature extraction and then age classification. In this paper, the Local Binary Pattern (LBP) algorithm is adopted to extract the face features focusing on selecting the best possible combination among all the features produced from the LBP algorithm. Feature Selection Method (FSM) is employed to increase the accuracy. FSM yields better results compared to other techniques’ results. Support Vector Machine (SVM) is used to classify the tested person image and assign that person to the related age. Results conducted using MATLAB produced accuracy of 93.81% with FSM technique compared to 81.61% without it. When damaged images are excluded from the database used for training, the accuracy is increased to 94.57%.
{"title":"Facial Features Extraction Using LBP for Human Age Estimation Based on SVM Classifier","authors":"N. F. Hasan, S. Q. Mahdi","doi":"10.1109/CSASE48920.2020.9142085","DOIUrl":"https://doi.org/10.1109/CSASE48920.2020.9142085","url":null,"abstract":"Research on age estimation witnessed increasing attention due to the demand for its applications. The age estimation has an essential role in preventing under-age persons from performing adult activities. The proposed age estimation technique is carried out through several stages; preprocessing, feature extraction and then age classification. In this paper, the Local Binary Pattern (LBP) algorithm is adopted to extract the face features focusing on selecting the best possible combination among all the features produced from the LBP algorithm. Feature Selection Method (FSM) is employed to increase the accuracy. FSM yields better results compared to other techniques’ results. Support Vector Machine (SVM) is used to classify the tested person image and assign that person to the related age. Results conducted using MATLAB produced accuracy of 93.81% with FSM technique compared to 81.61% without it. When damaged images are excluded from the database used for training, the accuracy is increased to 94.57%.","PeriodicalId":254581,"journal":{"name":"2020 International Conference on Computer Science and Software Engineering (CSASE)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124994124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-01DOI: 10.1109/CSASE48920.2020.9142093
Bazeer Ahamed B, R. Najimaldeen, Y. Duraisamy
In the present information age, providing correct information to the query with a limited concept is a challenging task. The modern adaptive Information Retrieval (IR) system is needed to provide valid information. Ontology-based on IR can provide a better solution. Ontology is a perception of shared conceptualization, represented by classes, properties, and objects. Ontology mapping offers a solution to integrate inter-domain knowledge. This paper presents a method for using ontologies to handle inter-domain query in information retrieval. Using ontology in IR for Query Expansion (QE) and document ranking seems to be the ultimate goal. The Multi-Domain Specific Ontology Mapping (MDOM) proposes the concepts derived from ontology for query expansion and information retrieval. The result shows the improvement in terms of better document ranking.
{"title":"Enhancement Framework of Semantic Query Expansion Using Mapped Ontology","authors":"Bazeer Ahamed B, R. Najimaldeen, Y. Duraisamy","doi":"10.1109/CSASE48920.2020.9142093","DOIUrl":"https://doi.org/10.1109/CSASE48920.2020.9142093","url":null,"abstract":"In the present information age, providing correct information to the query with a limited concept is a challenging task. The modern adaptive Information Retrieval (IR) system is needed to provide valid information. Ontology-based on IR can provide a better solution. Ontology is a perception of shared conceptualization, represented by classes, properties, and objects. Ontology mapping offers a solution to integrate inter-domain knowledge. This paper presents a method for using ontologies to handle inter-domain query in information retrieval. Using ontology in IR for Query Expansion (QE) and document ranking seems to be the ultimate goal. The Multi-Domain Specific Ontology Mapping (MDOM) proposes the concepts derived from ontology for query expansion and information retrieval. The result shows the improvement in terms of better document ranking.","PeriodicalId":254581,"journal":{"name":"2020 International Conference on Computer Science and Software Engineering (CSASE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128465318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：