M. I. Quraishi, G. Das, A. Das, P. Dey, A. Tasneem
{"title":"A novel approach for face detection using artificial neural network","authors":"M. I. Quraishi, G. Das, A. Das, P. Dey, A. Tasneem","doi":"10.1109/ISSP.2013.6526898","DOIUrl":null,"url":null,"abstract":"In recent time face detection is of utmost importance because for its various applications. Several approaches have been implemented to date. This paper aims towards an effort to represent a novel approach for human face recognition. The proposed system consists merging both frequency and spatial domain techniques. The proposed system selects the Region of Interest on which Ripplet Transformation is to be applied after power law transformation to calculate Standard Deviation (SD) and Mean as features. At later stage, Feed Forward Back Propagation Neural Network (FFBPNN) is used for classification and recognition purpose. The approach is tested with non face images to show its effectiveness which is around 91.67%.","PeriodicalId":354719,"journal":{"name":"2013 International Conference on Intelligent Systems and Signal Processing (ISSP)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 International Conference on Intelligent Systems and Signal Processing (ISSP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISSP.2013.6526898","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
In recent time face detection is of utmost importance because for its various applications. Several approaches have been implemented to date. This paper aims towards an effort to represent a novel approach for human face recognition. The proposed system consists merging both frequency and spatial domain techniques. The proposed system selects the Region of Interest on which Ripplet Transformation is to be applied after power law transformation to calculate Standard Deviation (SD) and Mean as features. At later stage, Feed Forward Back Propagation Neural Network (FFBPNN) is used for classification and recognition purpose. The approach is tested with non face images to show its effectiveness which is around 91.67%.