An approach to face recognition using Bayesian networks

Show simple item record Moise, Marian 2011-04-13T21:06:31Z 2011-04-13T21:06:31Z 2011-04-01
dc.description.abstract There are many categories of algorithms that tackle face recognition, one of them being based on Bayesian Networks which allow to encode causal relationships between different kind of random variables, thus helping to express correlations between salient facial features(eyebrows, eyes, nose, mouth). Although current algorithms are quite successful on controlled conditions, performance decreases rapidly in case of unconstrained viewing conditions, such as head pose and illumination for instance. In order to diminish the influence of lighting conditions, histogram equalization is used as a preprocessing algorithm. The used algorithm for features extraction from the grayscale image is the two-dimensional Cosine Transform (2D-DCT) and for facial features localization it has been used the Active Shape Models (ASM) which consists in fitting the shape of an object, using a previously learned global shape model, and represented as a set of landmark points on the face. The model of the used Bayesian Network can be explained as follow: the root node on the top represents a face (node F), which is composed of the relationships between eyebrows (node B), eyes (node E), the nose (node N) and the mouth (node M).And finally, these types of facial features generate the corresponding observations. Finally, we compare the proposed system with two popular appearance-based approaches: PCA (Principal Components Analysis or Eigenfaces) and LDA (Linear Discriminant Analysis or Fisherfaces). en_US
dc.language.iso en en_US
dc.publisher University of Regina Graduate Students' Association en_US
dc.relation.ispartofseries Session 2.4 en_US
dc.subject Face recognition en_US
dc.subject Bayesian networks en_US
dc.subject Facial feature extraction using 2D-DCT en_US
dc.subject Face features localization using ASM en_US
dc.subject Face-based authentication en_US
dc.title An approach to face recognition using Bayesian networks en_US
dc.type Presentation en_US
dc.description.authorstatus Student en_US
dc.description.peerreview yes en_US

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