{"title":"基于图像变换和多流CNN的熟练伪造签名识别与检测","authors":"Papiya Das, Swarnabja Bhaumik, Subhrapratim Nath","doi":"10.1109/vlsidcs53788.2022.9811485","DOIUrl":null,"url":null,"abstract":"Person identification through their credentials such as biometrics or signature is very important for one’s privacy and has become the most integral part for recognition. Prevention of forgeries in handwritten signatures has gain prominence in recent times. To serve this target this paper carried out Image Transformation techniques and an Artificial Intelligence model to effectively notice the differences of genuine and forged signature. Grass-fire transformations and optical flow captures the disparity in signatures. Proposed system uses Deep learning framework with ResNet 50 along with Convolutional Neural network (CNN). Comparative studies have been done using SVC2004 and SUSIG benchmark with the existing literature.","PeriodicalId":307414,"journal":{"name":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Signature Recognition and Detection of Skilled Forgeries Using Image Transformation and Multistream CNN\",\"authors\":\"Papiya Das, Swarnabja Bhaumik, Subhrapratim Nath\",\"doi\":\"10.1109/vlsidcs53788.2022.9811485\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Person identification through their credentials such as biometrics or signature is very important for one’s privacy and has become the most integral part for recognition. Prevention of forgeries in handwritten signatures has gain prominence in recent times. To serve this target this paper carried out Image Transformation techniques and an Artificial Intelligence model to effectively notice the differences of genuine and forged signature. Grass-fire transformations and optical flow captures the disparity in signatures. Proposed system uses Deep learning framework with ResNet 50 along with Convolutional Neural network (CNN). Comparative studies have been done using SVC2004 and SUSIG benchmark with the existing literature.\",\"PeriodicalId\":307414,\"journal\":{\"name\":\"2022 IEEE VLSI Device Circuit and System (VLSI DCS)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE VLSI Device Circuit and System (VLSI DCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/vlsidcs53788.2022.9811485\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE VLSI Device Circuit and System (VLSI DCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/vlsidcs53788.2022.9811485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Signature Recognition and Detection of Skilled Forgeries Using Image Transformation and Multistream CNN
Person identification through their credentials such as biometrics or signature is very important for one’s privacy and has become the most integral part for recognition. Prevention of forgeries in handwritten signatures has gain prominence in recent times. To serve this target this paper carried out Image Transformation techniques and an Artificial Intelligence model to effectively notice the differences of genuine and forged signature. Grass-fire transformations and optical flow captures the disparity in signatures. Proposed system uses Deep learning framework with ResNet 50 along with Convolutional Neural network (CNN). Comparative studies have been done using SVC2004 and SUSIG benchmark with the existing literature.