2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)最新文献

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Face Recognition Based on Windowing Technique Using DCT, Average Covariance and Artificial Neural Network 基于DCT、平均协方差和人工神经网络加窗技术的人脸识别

2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2018-10-01 DOI: 10.1109/ICIIBMS.2018.8549981

Divya A, K. Raja, V. R.

{"title":"Face Recognition Based on Windowing Technique Using DCT, Average Covariance and Artificial Neural Network","authors":"Divya A, K. Raja, V. R.","doi":"10.1109/ICIIBMS.2018.8549981","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549981","url":null,"abstract":"The field of Face Recognition (FR) is still a thought-provoking problem, while in recent advances of Artificial Neural Networks (ANN) has shown improved performance in FR rate. In this paper, we propose face recognition based on windowing technique using Discrete Cosine Transform (DCT), average covariance and ANN. The novel concept of windowing technique is used to divide each image to $mathbf{4x4},mathbf{8X8}$ and $mathbf{16X16}$ size of windows. The DCT is applied on each window to obtain DCT co-efficients. The covariance matrix is computed on each DCT coefficient matrix and average value of each block is also computed to obtain final feature value. The computation of an average covariance reduces the original size of face image by around 97% i.e., the number of co-efficients in the final feature set is only around 3% of the original size of an image. The proposed method is very efficient in identifying with very less number of features. Network is created and trained the input dataset and target dataset to reach the desired output. The trained net is then tested to compute performance parameters of the network. The experiments are conducted on some popularly used face databases to illuminate the performance and the efficiency of the proposed algorithm. The experimental results are tabulated and are compared with the existing methods. It is observed that, the proposed model achieves better recognition accuracy for $mathbf{16X16}$ windowing and also with existing algorithms.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129313240","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}

引用次数: 2

Rapid Human Body Detection in Disaster Sites Using Image Processing from Unmanned Aerial Vehicle (UAV) Cameras 基于无人机相机图像处理的灾害现场快速人体检测

2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2018-10-01 DOI: 10.1109/ICIIBMS.2018.8549955

Mbaitiga Zacharie, Satoshi Fuji, Shimoji Minori

{"title":"Rapid Human Body Detection in Disaster Sites Using Image Processing from Unmanned Aerial Vehicle (UAV) Cameras","authors":"Mbaitiga Zacharie, Satoshi Fuji, Shimoji Minori","doi":"10.1109/ICIIBMS.2018.8549955","DOIUrl":"https://doi.org/10.1109/ICIIBMS.2018.8549955","url":null,"abstract":"The development and impact of technology on our everyday lives cannot be compared with the world our ancestors lived in several decades ago. This is described as the world of technology (WoT). But despite all the advancements in technologies, understanding of the mechanisms of nature and the damages caused via natural disasters, such as earthquakes, landslides, and flooding to mention only a few, are still very far away. In the effort of saving lives during natural disasters, such as earthquakes, this study introduces a rapid human body detection using image processing from UAV camera. The skin color from a female student is first extracted in RGB then converted to HSV. Next, opening and closing morphological operations are performed eight times each to remove all noise present in the image. Experimental tests were performed both indoor and outdoor, where the female student presented an object close and far to the camera to check the detection capability in both cases. The experiment results show that close or far, the camera can clearly detect both a human body and any part of a human body. The results of the experiment proves the merit of the proposed method.","PeriodicalId":430326,"journal":{"name":"2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128359057","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}

引用次数: 16

Prediction of Visitors using Machine Learning 使用机器学习预测访客

2018 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) Pub Date : 2018-10-01 DOI: 10.1109/ICIIBMS.2018.8549960

Kyoungho Son, Yungcheo l Byun, Sang-Joon Lee

引用次数: 0

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