Lips Recognition for Biometric Identification Systems

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dc.contributor.authorBoucetta, Aldjia
dc.contributor.authorBoussaad, Leila
dc.date.accessioned2024-03-12T18:07:36Z
dc.date.available2024-03-12T18:07:36Z
dc.date.issued2021-05-25
dc.description.abstractIn recent years, researches in biometric methods have gained much attention and they have advanced to a wide scope in security concepts. Therefore, many biometric technologies have been developed and enhanced with many of the most successful security applications. Lately, lip-based biometric identification becomes one of the most relevant emerging tools, which comes from criminal and forensic real-life applications. The main purpose of this paper is to prove the benefit of lips as a biometric modality, by using both handcraft and deeplearning based feature extraction methods. So, we consider three different techniques, Histogram of Oriented Gradients(HOG), Local Binary Pattern(LBP) and pretrained Deep-CNN. All results are confirmed by a ten-fold cross-validation method using two datasets, NITRLipV1 and database1. The mean accuracy is found to be very high in all the experiments carried out. Also the feature extraction using the Inceptionv3 model always achieve highest mean accuracy.
dc.identifier.isbn978-9931-9788-0-0
dc.identifier.urihttp://dspace.univ-oeb.dz:4000/handle/123456789/18726
dc.language.isoen
dc.publisherUniversity of Oum El Bouaghi
dc.subjectHuman identification; lips recognition; histogram of oriented gradients (HOG); local binary Pattern (LBP); convolutional neural network (CNN).
dc.titleLips Recognition for Biometric Identification Systems
dc.typeArticle
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The Second International Conference on Computer Science's Complex Systems and their Applications (Oum El Bouaghi, Algeria, May 25-26, 2021)