Automatic Age Estimation and Interactive Museum Exhibits

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Automatic Age Estimation and Interactive Museum Exhibits. Kyle Patterson. Advisors: Prof. Cass and Prof. Lawson. Motivation. Museums have begun to use more technology in interactive exhibits. Interactions are still general for all users. Research Problem. - PowerPoint PPT Presentation

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Automatically Age Estimation and Museum Exhibits

Kyle PattersonAutomatic Age Estimation and Interactive Museum ExhibitsAdvisors: Prof. Cass and Prof. Lawson

MotivationMuseums have begun to use more technology in interactive exhibits.Interactions are still general for all users.

Research ProblemChange the content of museum exhibits to match the age of their audience.

Research ProblemChange interactive museum exhibits to the age of their audience.

Children ShownAdults ShownPossible SolutionsPhysical user input

Possible SolutionsPhysical user inputUser must understand and use the interface.

Possible SolutionsPhysical user inputUser must understand and use the interface.

Audio user input

Possible SolutionsPhysical user inputUser must understand and use the interface.

Audio user inputLanguage dependent, must isolate users answer.

Possible SolutionsPhysical user inputUser must understand and use the interface.

Audio user inputLanguage dependent, must isolate users answer.

Gait analysis

Possible SolutionsPhysical user inputUser must understand and use the interface.

Audio user inputLanguage dependent, must isolate users answer.

Gait analysisRequires video input and human tracking from multiple angles.

Project GoalClassify humans by age group using facial images.ChildYoung AdultSenior

ApproachTest ImageTraining ImagesTraining DatasetTest DatasetClassifierAge GroupFeature ExtractionFeature ExtractionMachine Learning AlgorithmData CollectionUse video or stills to provide images of the user.

Extract usable data from the images.

Output data to classification program.

Principal Component AnalysisPCA reduces the complexity of data by reducing the number of features in the dataset.The result is a series of vectors for each image.

EvaluationTraining and test images from the Center for Vital Longevity Face Database.

Evaluation of the approach is carried out using 10-fold cross validation.

Kennedy, K. M., Hope, K., & Raz, N. (2009). Lifespan Adult Faces: Norms for Age, Familiarity, Memorability, Mood, and Picture Quality. Experimental Aging Research, 35(2), 268-275.

ResultsPCA Performed Over 5 DimensionsSuccessful Classification PercentageAge GroupsOnly MalesOnly FemalesBoth Sexes18 - 3390.978.068.834 - 6416.622.225.065 - 9278.564.572.8All Ages72.262.260.2ResultsPCA Performed Over 5 DimensionsSuccessful Classification PercentageAge GroupsOnly MalesOnly FemalesBoth Sexes18 - 3390.978.068.834 - 6416.622.225.065 - 9278.564.572.8All Ages72.262.260.2ResultsPCA Performed Over 5 DimensionsSuccessful Classification PercentageAge GroupsOnly MalesOnly FemalesBoth Sexes18 - 3390.978.068.834 - 6416.622.225.065 - 9278.564.572.8All Ages72.262.260.2ResultsPCA Performed Over 5 DimensionsSuccessful Classification PercentageAge GroupsOnly MalesOnly FemalesBoth Sexes18 - 3390.978.068.834 - 6416.622.225.065 - 9278.564.572.8All Ages72.262.260.2ResultsPCA Performed Over 5 DimensionsSuccessful Classification PercentageAge GroupsOnly MalesOnly FemalesBoth Sexes18 - 3390.978.068.834 - 6416.622.225.065 - 9278.564.572.8All Ages72.262.260.2ResultsPCA Performed Over 5 DimensionsSuccessful Classification Percentage Per GroupAge GroupsOnly MalesOnly FemalesBoth Sexes18 - 3390.978.068.834 - 6416.622.225.065 - 9278.564.572.8All Ages72.262.260.2FisherfacesAlgorithm designed to perform facial recognition between defined classes.Uses Linear Discriminant Analysis to find the features that define the difference between classes.

dtreg.com/lda.htm

Opencv.org

Future WorkClassify gender before determining age.

Multimodal support for determining gender or age.

Webcam support for museum websites.Any Questions?