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The difference between a ‘bad’ photo and a ‘good’ photo is often a matter of whether or not the person in the photo is smiling. With the help of feature recognition and corner detection, we can identify smiles in a photo, and determine whether or not it is good.

Introduc4on0

Objec4ve0

Method0Plots of mouth corner detection points

Results0 Conclusion0

Using feature recognition and corner detection, we were able to successfully identify smiles that show teeth very accurately. We found closed mouth smiles were harder to detect. With our procedure closed mouth smiles were often categorized as inconclusive. Our system could prove to be a helpful application in digital photography, where it could be used to automatically select the best image in a set of similar images. Our system would be made even more useful if it were extended to work with video. Among possible video applications is marketing analysis of customer reaction.

References0 M. Jones and P. Viola, “Robust real-time object detection,” Workshop on Statistical and Computational Theories …, 2001.

S. Jianbo and C. Tomasi, “Good Features to Track,” 1994.

M. Castrillón, O. Déniz, C. Guerra, and M. Hernández, “ENCARA2: Real-time detection of multiple faces at different resolutions in video streams,” Journal of Visual Communication and Image Representation, vol. 18, no. 2, pp. 130–140, Apr. 2007.

Contact0Informa4on0Justin DeVito: jd5@rice.edu Amanda Meurer: amm10@rice.edu Daniel Volz: djv2@rice.edu

Automatically identify the best photo of a person based on their smile. Average number of mouth corner detections

and average concavity. Data collected from 200 subjects.

Line of best fit for a smiling face

Line of best fit for an unsmiling face

Number0of0Mouth0Corner0Detec4ons0

Concavity0of0the0Line0of0Best0Fit0

Smiling0Face0 16.3" 0.0124"

Unsmiling0Face0 7.7" 0.0016"

Jus.n"DeVito,"Daniel"Volz,"Amanda"Meurer"

Smile"Iden.fica.on"Via"Feature"Recogni.on"and"Corner"Detec.on"

Background0

310 320 330 340 350 360 370 380 390−315

−310

−305

−300

−295

−290

−285

−280

−275

−270

−265Detection of Curvature

260 280 300 320 340 360 380 400−400

−390

−380

−370

−360

−350

−340Detection of Curvature

Based"on"the"concavity"and"corner"density,"decide"whether"the"subject"is"smiling."

Find"the"corner"density"of"the"mouth"region."

Calculate"concavity"of"detected"image"corners."

Detect"mouth"corners"using"ShiSTomasi"algorithm."

Crop"out"the"boVom"third"of"the"face."

Find"faces"using"a"modified"ViolaSJones"algorithm."

Input"faces"

Viola-Jones Feature Recognition Algorithm: Scan the image with Haar features. From their response to the image, determine where the face is. Shi-Tomasi Corner Detection Algorithm:

I ~ Intensity of the Window R ~ Corner Significance Parameter ~ Eigenvalues of M

Example Haar Features (Viola, 2001)

Number00(200)" Total0%"

Procedure0Validity"

Correct0Recogni4ons" 121" 61%" 93%"

False0Posi4ves" 9" 5%" 7%"

Inconclusive" 70" 35%"

Statistical Analysis of 200 subjects. Each subject has one smiling and one non-smiling photo.

0 2 4 6 8 10 12 14 16 180

2

4

6

8

10

12

14

16

18

20

Detected Corners for Non!smiling Photos

Number of Detected Corners

Fre

qu

en

cy (

of

20

0)

!0.015 !0.01 !0.005 0 0.005 0.01 0.015 0.02 0.0250

0.5

1

1.5

2

2.5

3

3.5

4

Mouth Curvature for Non!smiling Photos

Concavity Parameter Value

Fre

quency

(of 200)

0 5 10 15 20 25 30 35 400

2

4

6

8

10

12

14

16

18

20

Detected Corners for Smiling Photos

Number of Detected Corners

Fre

qu

en

cy (

of

20

0)

!0.06 !0.04 !0.02 0 0.02 0.04 0.060

10

20

30

40

50

60

70

80

90

Mouth Curvature for Smiling Photos

Concavity Parameter Value

Fre

qu

en

cy (

of

20

0)

Maximum Frequency for Non-Smiling Concavity is 4 Maximum Frequency for Smiling Concavity is 90

Smile Recognition using Feature and Corner Detection AlgorithmsPurpose:We want to automatically detect a smiling subject in a picture. Our intended

application is for digital photography, where this algorithm can be applied toautomatically select the best image in a set of similar images. This would reducethe time a photographer would need to spend looking through images from aphoto shoot by automatically discarding images where the subject isn’t smiling.

First we detect the face using a Viola-Jones based algorithm. This algorithmis outlined by Lienhart, Kuranov, and Pisarevsky (Ref. XXXX)

asdfShi-Tomasi corner detection is based upon Harris-Stephens corner detection,

just with different threshold parameters.The Harris corner detector operator is defined as:

E(u, v) =X

x

X

y

w(x, y) [I (x+ u, y + v)� I (x, y)]2

• E - Sum of squared differences between the original and moved window

• u - x direction window displacement

• v - y direction window displacement

• w(x, y) - window at position (x,y)

• I(x+ u, y + v) - intensity of the moved window

• I(x, y) - intensity of the original window

We wish to maximize the E term. In order to do this, we use a first order Taylorseries approximation of I:

E(u, v) ⇡X

x

X

y

w(x, y) [I (x, y) + uI

x

+ vI

y

� I (x, y)]2

E(u, v) ⇡X

x

X

y

w(x, y)�u

2I

2x

+ 2uvIx

I

y

+ v

2I

2y

�

Then changing to a matrix representation gives:

E(u, v) ⇡⇥x y

⇤X

x

X

y

w(x, y)

I

2x

I

x

I

y

I

x

I

y

I

2y

� x

y

�

M is the structure tensor from above.

M =X

x

X

y

w(x, y)

I

2x

I

x

I

y

I

x

I

y

I

2y

�

The determination of R, which is the corner importance measurement isdone by taking the minimum of the two eigenvalues of this matrix.

1

R = min(�1,�2)

where �1,�2 are eigenvalues of M.

This is the Shi-Tomasi modification of the Harris and Stephens corner de-tection algorithm. (Good Features to Track)

2

R = min(�1,�2)

where �1,�2 are eigenvalues of M.

This is the Shi-Tomasi modification of the Harris and Stephens corner de-tection algorithm. (Good Features to Track)

2