android app for identifying digital signage...

IntroductionCollecting dataPixel analysisTraining data

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Android App for Identifying Digital Signage Viewer

Dane Hylton

Kennesaw State University

April 27, 2017

Dane Hylton Android App for Identifying Digital Signage Viewer


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Race detection

E-Tech software predicts ethnicity based on person’s last name

Kairos: http://kairos.com/diversity-recognition



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Ethnicities used:

African/black

Asian

Indian

Hispanice/Latino

Midille Esastern

White/Caucasian



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Data collection

The data was comprised of face pictures taken from theinternet.

20 data sets of each race were collected.

Each data set included 10 male and female.

Each picture were then editted to get as close to the face aspossible (this was done to avoid background pixel data).



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Samples

Original Image Edited image



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Finding the most frequent pixels

Objective: find the most frequent pixels (frequency pixels)

Finding the most occuring pixles for each race gives and ideaof what pixel values to look for when detecting race.

Images were processed in Matlab and stored in their originaldimensions.

A matrix was formed for each race with their frequency pixels.



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Converting from RGB to HSV

HSV color

Named for 3 values: hue, saturation, and value. Why useHSV?

To analyze the pixels and sort them interms of their tone.

Will be easier to find specific range of colors.



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Sorting HSV colors



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Intervals of HSV colors for detecting race

For the 20 frequent pixels of each race the average of thebottom half (abh) and the top half was (ath).

A detected face would need to be in this range to detectsomeone’s race.



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Sample data with intervals: abh tbh





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Sample

Sorted HSV pixels for Africanimage data

Upper and lower bound forafrican data



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Sample data with intervals: abh tbh





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Neural network architecture



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Weight training

Error term for ouput

δOk = Ok(E )(1 − ok(E ))(tk(E ) − Ok(E ))

where Ok(E ) is the ouput, and (tk is for categorization.

Error term for hidden unit.

δhk = hk(E )(1 − hk(E )) ·∑

i∈outputs(wkiδOi

)



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Calculating new weights

For each weight wij .

Between input unit i and and hidden unit j . The learning raten = 0.7.

∆ij = nδhj xi

Between hidden unit i and output unit j.

∆ij = nδOjhi (E )

where hi (E ) is the output from the hidden unit i for E.



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Weights obtained

w1 = 0.8108w2 = 0.0121w3 = 0.0730w4 = 0.3268w5 = 0.0613w6 = -0.9008w7 = 0.5139w8 = 1.0427w9 = 0.4881w10 = 1.0745w11 = -0.1855w12 = 0.6990w13 = 0.6308w14 = -0.3979w15 = 0.4430w16 = 0.5668w17 = 0.9113w18 = 1.0292



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Results



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Conclusion

Race detection is very hard!

A lot of variable to take into consideration.

Neural networks are hard to train especially for novices.

No known data/algorithm for detecting race/ethnicity is opensource.



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Live DEMO

Questions?

Live Demo


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