2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)最新文献

{"title":"Fly wing biometrics","authors":"Michael E. Payne, Jonathan Turner, Joseph Shelton, Joshua Adams, J. Carter, Henry Williams, Caresse Hansen, I. Dworkin, G. Dozier","doi":"10.1109/CIBIM.2013.6607912","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607912","url":null,"abstract":"Genetic and Evolutionary Feature Extraction (GEFE), introduced by Shelton et al. [1], [2], [3], use genetic and evolutionary computation to evolve Local Binary Pattern (LBP) based feature extractors for facial recognition. In this paper, we use GEFE in an effort to classify male and female Drosophila melanogaster by the texture of their wings. To our knowledge, gender classification of the drosophila melanogaster via its wing has not been performed. This research has the potential to simplify the work of geneticists who work with the drosophila melanogaster. Our results show that GEFE outperforms both LBP and Eigenwing methods in terms of accuracy as well as computational complexity.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132156976","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}

{"title":"Person identification from actions based on Artificial Neural Networks","authors":"Alexandros Iosifidis, A. Tefas, I. Pitas","doi":"10.1109/CIBIM.2013.6607907","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607907","url":null,"abstract":"In this paper, we propose a person identification method exploiting human motion information. A Self Organizing Neural Network is employed in order to determine a topographic map of representative human body poses. Fuzzy Vector Quantization is applied to the human body poses appearing in a video in order to obtain a compact video representation, that will be used for person identification and action recognition. Two feedforward Artificial Neural Networks are trained to recognize the person ID and action class labels of a given test action video. Network outputs combination, based on another feedforward network, is performed in the case of multiple cameras used in the training and identification phases. Experimental results on two publicly available databases evaluate the performance of the proposed person identification approach.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116727796","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}

{"title":"Real-time beard detection by combining image decolorization and texture detection with applications to facial gender recognition","authors":"Jian-Gang Wang, W. Yau","doi":"10.1109/CIBIM.2013.6607915","DOIUrl":"https://doi.org/10.1109/CIBIM.2013.6607915","url":null,"abstract":"There are still many challenging problems in facial gender recognition which is mainly due to the complex variances of face appearance. Although there has been tremendous research effort to develop robust gender recognition over the past decade, none has explicitly exploited the domain knowledge about the appearance difference between male and female. Beard/mustache contributes substantially to the facial appearance difference between male and female and could be a good feature to be incorporated into facial gender recognition. Little work on beard segmentation has been reported in the literature. In this paper, a novel real-time beard/mustache detection method is proposed which combines face feature extraction, image decolorization and texture detection. Image decolorization, which converts a color image to grayscale, aims to enhance the color contrast while preserving the grayscale. On the other hand, beard appearance is normally grayscale surrounded by the skin color face tissue. Hence, it is a fast and efficient way to segment the beard by using the decolorization technology. In order to make the algorithm robust to the variances of illumination and head pose, an adaptive decolonization segmentation has been proposed in which both the segmentation threshold selection and the beard region following are guided by some special regions defined by their geometric relationship with the salient facial feature. Furthermore, a texture-based beard classifier is developed to compensate the decolonization-based segmentation which could detect the darker skin or shadow around the mouth caused by the small lines or skin thicker from where he/she smiles as beard. Only the face is verified as the face contains beard/mustache when it satisfies: 1) a larger beard region can be found by applying the decolonization segmentation; 2) the segmented beard region is detected as beard by the texture beard detector. The experimental results on color FERET database have shown that the proposed approach can achieve 89% bearded face detection rate with 0.1% false acceptance rate.","PeriodicalId":286155,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence in Biometrics and Identity Management (CIBIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130875600","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}