U.S. patent application number 11/856137 was filed with the patent office on 2008-01-03 for automatic attractiveness rating machine.
Invention is credited to Amnart Kanarat.
Application Number | 20080004109 11/856137 |
Document ID | / |
Family ID | 38877384 |
Filed Date | 2008-01-03 |
United States Patent
Application |
20080004109 |
Kind Code |
A1 |
Kanarat; Amnart |
January 3, 2008 |
AUTOMATIC ATTRACTIVENESS RATING MACHINE
Abstract
The present invention teaches an amusement machine having the
abilities of automatically estimating the degree of attractiveness
of a user facial image. The amusement machine has a camera 11 for
taking a facial image of the user and assigns the attractiveness
score. The result is then displayed to the user on a computer
screen 13.
Inventors: |
Kanarat; Amnart;
(US) |
Correspondence
Address: |
Mr. Amnart Kanarat
337/80 M. Sethsiri Sanambinnam Rd.
T. Tasai
Muang, Nonthaburi
11000
TH
|
Family ID: |
38877384 |
Appl. No.: |
11/856137 |
Filed: |
September 17, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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10329653 |
Dec 26, 2002 |
7286692 |
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11856137 |
Sep 17, 2007 |
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Current U.S.
Class: |
463/30 |
Current CPC
Class: |
G06Q 20/18 20130101 |
Class at
Publication: |
463/030 |
International
Class: |
A63F 9/24 20060101
A63F009/24 |
Claims
1. A method of playing a game comprising the steps of: obtaining a
digital facial image of a user; determining an attractiveness score
of the digital facial image of the user; and notifying the user of
the attractiveness score.
2. The method as defined by claim 1, wherein the step of
determining the attractiveness score comprises: extracting
geometrical features from the digital facial image; and determining
a degree of attractiveness based upon the extracted geometrical
features.
3. The method as defined by claim 1, wherein the degree of
attractiveness is a numerical value within a prescribed range.
4. The method as defined by claim 1, wherein the step of
determining comprises: determining a set of geometrical features of
the digital facial image of the user; assigning a value to each of
the geometrical features of the digital facial image of the user;
and determining an attractiveness of the digital facial image of
the user based upon the values assigned to each of the geometrical
features.
5. The method as defined by claim 4, wherein the step of
determining an attractiveness of the digital facial image includes
using an artificial neural network.
6. The method as defined by claim 5, further comprising the step of
training the artificial neural network using a training set prior
to determining an attractiveness score.
7. The method as defined by claim 6, wherein the training comprises
associating attractiveness values to geometrical features.
8. A method of rating a degree of attractiveness of a facial image,
comprising the steps of: measuring geometrical features of said
facial image; presenting said geometrical features at the input
layer of a trained artificial neuron network; and receiving said
degree of attractiveness of said facial image at the output layer
of said trained artificial neuron network.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This is a division of Non-provisional patent application
Ser. No. 10/329,653, filed Dec. 26, 2002.
OTHER REFERENCES
[0002] Brunelli, R. and Poggio, T., Face Recognition: Features
versus Templates, IEEE Transaction on Pattern Analysis and Machine
Intelligence, Vol. 15, No. 10, October 1993, pp. 1042-1052.
STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
[0003] Not Applicable
REFERENCE TO SEQUENCE LISTING, TABLE, OR COMPUTER PROGRAM
LISTING
[0004] Not Applicable
BACKGROUND
[0005] 1. Field of Invention
[0006] This invention relates to an amusement machine, specifically
to such amusement machine which is capable of rating the degree of
attractiveness of a user from his/her facial image.
[0007] 2. Description of Prior Art
[0008] Nowadays, in the computer technology era, a lot of
computerized entertainment machines are receiving a lot of
attentions from many people. As we can see that there are
increasing numbers of amusement machines such as arcade games,
sticker kiosks, or automatic photography booths available in many
public places like theme parks and shopping malls. These machines
serve the need of a user in providing amusement to the user.
However, people normally get bored very quickly with these
amusement machines, so they constantly look for a new kind of
entertainment, especially a bizarre one. The present invention aims
to offer users with a unique kind of entertainment which has never
been introduced before.
[0009] At the present time, all of the inventions relate to the
field of amusement machines involving the manipulation of user
facial images are found in the form of automatic photograph booths,
for example, U.S. Pat. No. 6,148,148 (2000) and U.S. Pat. No.
6,229,904 (2001). The operation of these booths typically involves
users sitting or standing in front of a digital/analog camera to
have their images taken. The images are manually or automatically
manipulated to modify image attributes such as
background/foreground or to blend multiple images together. The
manipulated images are developed and then dispensed to the users.
However, none of these conventional automatic photograph booths has
the capability of analyzing facial images and matching them against
the facial images stored in a database as the present invention
does. Moreover, none of the amusement machines in the prior art has
the ability to assign an approximate quantitative value to a
qualitative attribute, such as the attractiveness of a person face,
as the present invention does.
SUMMARY
[0010] In accordance with the present invention a machine comprises
a computer, a camera, a computer screen, a track ball, a select
button, a lighting means, a coin/bill receptor, audio speakers, a
background curtain, a method of facial image matching, and a method
of attractiveness rating.
[0011] The machine, an automatic celebrity face matching and
attractiveness rating machine, automatically analyzes a facial
image of the individual, and compares it to a collection of the
celebrity facial images previously stored in the machine's computer
to identify which celebrity most closely resembles the individual.
Furthermore, the facial image is analyzed to establish a measure of
the attractiveness of the individual. Both the matching result and
the attractiveness score are reported to the individual.
OBJECTS AND ADVANTAGES
[0012] This invention aims to offer users a new kind of
entertainment by providing answers to some trivial questions that a
curious individual might have. Ones might wonder which celebrity
they look like or how good they look. Here, the present invention
is created to answer these two trivial questions to an individual
in an interesting and humorous fashion.
[0013] Accordingly, several objects and advantages of the present
invention are:
[0014] (a) to provide a machine which is mainly used for amusement
purpose.
[0015] (b) to provide an amusement machine which can report to an
individual which celebrity the individual looks like the most.
[0016] (c) to provide an amusement machine which can report to an
individual approximately how much attractiveness the individual
is.
[0017] (d) to provide a method in assigning an approximate
quantitative value to a qualitative attribute such as the
attractiveness of a person face.
[0018] (e) to provide such amusement machine which can be used to
generate income.
[0019] Further objects and advantages of the present invention will
become apparent from a consideration of the ensuing description and
drawings.
DRAWING FIGURES
[0020] FIG. 1 is a diagrammatic perspective view of one form of a
machine according to the invention.
[0021] FIG. 2 shows a front view of the machine.
[0022] FIG. 3 shows a rear view of the machine.
[0023] FIG. 4 illustrates a component diagram of the machine.
[0024] FIG. 5 illustrates a flowchart of the main computer program
of the machine.
[0025] FIG. 6 illustrates a flowchart of the face matching
program.
[0026] FIG. 7 illustrates a diagram of the degree of attractiveness
estimator.
REFERENCE NUMERALS IN DRAWINGS
[0027] TABLE-US-00001 11 camera 12 lighting means 13 computer
screen 14 small speaker 15 track ball 16 selectz button 17
coin/bill receptor 18 background curtain 19 main speaker 20
computer 21 base section 22 neck section 23 head section 24 cooling
fan 25 base section lid 26 power cord 27 power cord entry 28
curtain controller
DESCRIPTION
[0028] A preferred embodiment of the machine of the present
invention is illustrated in FIG. 1. FIG. 1 is an isometric view of
one form of the machine constructed in accordance with the
invention. The machine is in the form of a floor-standing type, and
comprises a base section 21, a neck section 22, a head section 23,
and a curtain controller 28. The base section 21, made of painted
wood board or metal sheet, has a flat portion in the front to serve
as a designated area for a person to stand on. A cooling fan 24 is
installed on one side of the base section 21 to provide enough
ventilation within the base section 21 to prevent a computer 20
mounted inside the base section 21 from overheating. A power cord
26 comes out from the back of the base section 21. Its function is
to provide enough electricity needed by the machine. The computer
20 is a Personal Computer with at least one Gigahertz
Microprocessor and at least 128 Megabytes of Random-Access Memory
(RAM). The operating system of the computer 20 can be any operating
system, preferably either WINDOWS or LINUX.
[0029] The neck section 22, which is also made of painted wood
board or metal sheet, has a main speaker 19, with a built-in audio
amplifier, on each side of the neck section 22. The main speaker 19
is an outdoor-type audio speaker, which is waterproof. The neck
section 22 also has a coin/bill receptor 17 located in the front.
The coin/bill receptor 17 functions like a standard coin/bill
receptor, which can sense how much money a user put in and dispense
changes back to the user if the amount of money the user put in
over the programmed limit. The coin/bill receptor 17 has a key lock
and can be accessed by only authorized persons.
[0030] The head section 23, which is also made of painted wood
board or metal sheet, contains a computer screen 13, a lighting
means 12, a track ball 15, a select button 16, and two small
speakers 14. A camera 11 is mounted on top of the head section 23,
and its field of view covers the whole area of a background curtain
18, which can be manually pulled down by a user or automatically
dropped by the curtain controller 28. The color of the background
curtain 18 is a solid color, most preferably a dark tone color such
as blue or green. The background curtain 18 can be made of cloth or
plastic. The curtain controller 28 controls the elevation of the
background curtain 18 according to a command from the computer
20.
[0031] FIG. 2 shows a front view of the machine that the user will
see when standing on the designated area between the camera 11 and
the background curtain 18. The camera 11 is a standard
fixed/auto-focus charge-coupled device (CCD) camera or a digital
video camera with resolution at least 640 by 480 pixels, and can be
either color or monochrome camera. A regular analog video camera
can also be used; however, a frame grabber must be installed in the
computer 20 to digitize a single frame of video into a digitized
frame. The computer screen 13 can be a regular color computer
monitor or a color computer LCD screen. Beside the computer screen
13 are located the lighting means 12, most preferably a fluorescent
lighting means, for providing enough illumination of a user face to
the camera 11.
[0032] A computer pointing device, the track ball 15 in this
present embodiment, provides the user the control of the motion of
a pointer displayed on the computer screen 13. The function of the
track ball 15 is similar to a regular pointing device, called
"mouse", used with a personal computer. The user can choose
whatever options displayed to the user on the computer screen 13 by
gently spinning the track ball 15 until the pointer locates on top
of the option the user would like to choose. Then, the user pushes
on the select button 16 to choose that option. The select button 16
is a regular push button that closes a circuit when the button is
pushed. In general, both the track ball 15 and the select button 16
can be interchangeable with other devices such as an electrostatic
touch-pad, which is widely used in notebook computers. In a
different embodiment of the present invention, both the track ball
15 and the select button 16 can be totally eliminated by replacing
the computer screen 13 with a touch-screen computer monitor, which
the user can choose the options displayed on the computer screen by
directly touching them. The two small speakers 14 are standard
computer speakers with built-in audio amplifiers, and are
waterproof.
[0033] FIG. 3 shows a rear view of the machine without showing the
curtain controller 28 at the top of the figure. A base section lid
25 is used to conceal the computer 20 located inside the base
section 21 from unauthorized access from outside. The machine owner
or administrator can access the computer 20 through the base
section lid 25 to make changes or update the program stored in the
hard disc of the computer 20. A power cord entry 27 is a
two-centimeter circular hole, which is located to the right of the
base section lid 25. The power cord 26 passes the power cord entry
27 into the base section 21, and connects to a power supply of the
computer 20. This power supply provides electricity to not only the
computer 20 but also the camera 11, the lighting means 12, the
computer screen 13, the small speaker 14, the track ball 15, the
select button 16, the coin/bill receptor 17, the main speaker 19,
the cooling fan 24, and the curtain controller 28, as shown in
FIGS. 1 and 2.
[0034] FIG. 4 illustrates a component diagram of the machine. It
shows how each component, which has been previously mentioned in
above paragraphs, in the machine connects to each other. It also
shows the flow direction of the data between a pair of components
which are connected to each other. It is obvious from the diagram
that every component is connected to the computer 20. The camera 11
is connected to the computer 20, and provides a single digital
image to the computer 20 whenever the camera 11 receives a command
from the computer 20. The arrow connecting between the camera 11
and the computer 20 shows the data flow in both directions.
[0035] Both the track ball 15 and the select button 16 are also
connected to the computer 20, and provide the pointer motion and
the user select inputs to the computer 20, respectively. There is
only one-way communications between the track ball 15 and the
computer 20, which is also true for the select button 16;
therefore, the arrow connecting between the track ball 15 and the
computer 20 shows the data flow in only one direction, which is
toward the computer 20. The coin/bill receptor 17 is also connected
to the computer 20. It provides an input to the computer 20 as to
how much money has been sensed at the coin/bill receptor 17. Once,
the amount of money sensed at the coin/bill receptor 17 is equal to
or more than the preprogrammed amount. The computer 20 sends
command to the coin/bill receptor 17 to reject all coin or bill
that is further put in by the user. If change is needed, the
computer 20 instructs the coin/bill receptor 17 to return the
change to the user.
[0036] The lighting means 12, the computer screen 13, the small
speaker 14, the main speaker 19, and the curtain controller 28 are
also directly connected to the computer 20. All of them receive
commands from the computer 20, but none of them send any input back
to the computer 20, which can be clearly seen from the directions
of the arrows shown in the figure. The lighting means 12 is turned
on or off by the command sent from the computer 20. The computer
screen 13 and the small speaker 14 are used by the computer 20 as a
means for communicating with the user. The main speaker 19 is
mainly used by the computer 20 for advertisement purpose. The
curtain controller 28 receives an elevation command from the
computer 20, and adjusts the height of the background curtain 18
accordingly.
[0037] FIG. 5 illustrates a flowchart of the main computer program
controlling the operation of the machine. This computer program
resides in the computer 20. The program starts by step S1 senses
money at the coin/bill receptor 17. After enough money has been
sensed, step S2 instructs the machine to automatically lower the
background curtain 18. Next, step S3 directs the user to look
straight at the camera 11 by displaying a message on the computer
screen 13. When the user is ready, he/she presses the select button
16 to take his/her frontal facial image according to the message on
the computer screen 13 issued by step S4. In step S5, the image is
temporarily stored in the computer hard disc as a Tagged Image File
Format (TIFF).
[0038] Before the face matching process begins, step S6 instructs
the machine to display a message asking the user to select a
category of the existing image databases, e.g., actors, actresses,
politicians, singers, sports stars, etc. Once the user has selected
the category in step S6, the face recognition or matching program,
whose detailed flowchart is shown in FIG. 6, is utilized in step S7
to identify a face in the selected image database yielding the
closest match.
[0039] Next, in step S8, the geometrical features are input into
the degree of attractiveness estimator, which will be discussed in
detail later in FIG. 7. The degree of attractiveness estimator is
then produced the approximate number indicating the degree of
attractiveness. This number can range from 0 to 100, where 100
means very attractive and 0 mean very unattractive. Finally, in
step S9, the machine displays both results, the most similar
celebrity and the degree of attractiveness, on the computer screen
13, and holds the results for one minute. After step S9 is
completed, the main program automatically returns to step S1 and
the whole processes are repeated all over again.
[0040] FIG. 6 illustrates a flowchart of the face matching program.
First, in process P1, the program reads a user facial image from
the computer hard disc, and temporarily stores the user facial
image in the computer memory. Next, in process P2, the format of
the user facial image is converted from the red-green-blue (RGB)
format into the intensity (gray-scale) format. This can be
accomplished by assigning the intensity value of each pixel in the
intensity image with the average value of the intensity values of
the three colors of the corresponding pixel in the RGB image.
[0041] Next, in process P3, facial features (such as eyes, nose,
mouth, and chin) are automatically extracted from the user facial
image in the intensity format. The facial feature extraction
method, which is described in the paper titled "Face Recognition:
Features versus Templates" by R. Brunelli and T. Poggio in IEEE
Transactions on Pattern Analysis and Machine Intelligence, Volume
15, Number 10, October 1993, is used to extract the facial
features. In total, thirty-five facial features are extracted from
the user facial image. These facial features are basically numbers
representing relative positions and sizes of each facial feature in
the user facial image.
[0042] Next, in process P4, the above thirty-five facial features
are normalized by the face width, which is measured at nose
position. The normalized facial features now become geometrical
features, P5, of the user facial image. These geometrical features,
P5, are very important. They are not only used in the following
matching process P6, but also used in the following attractiveness
rating process, which is discussed in FIG. 7. In process P6, the
matching is obtained by computing the Euclidean norms of all
vectors originating from the vector of the geometrical features of
the user facial image to those of the celebrity facial images
previously stored in the computer hard disc. The celebrity facial
image corresponding to the smallest Euclidean norm is the closest
match, and that celebrity facial image is declared in process P7 to
be the most similar face to the user facial image.
[0043] FIG. 7 illustrates a diagram of the degree of attractiveness
estimator. The main function of the estimator is to assign a number
to each set of geometrical features of a facial image presented to
the estimator. The number is then used as an attractiveness measure
or an estimated attractiveness score, where a higher number
indicates more attractive or desirable than a lower number. The
number, however, is only a coarse estimate since qualitative
attributes such as attractiveness and goodness in fact are not
measurable.
[0044] The estimator comprises an artificial neural network,
commonly referred to as a Multi-Layer Perceptron (MLP) as indicated
in box B2. The MLP used in the present invention has four layers:
one input layer, two hidden layers, and one output layer. The four
layers are fully connected to form the MLP. The input layer has
thirty-five input nodes, which is equal to the number of
geometrical features obtained from process P5. Each of the hidden
layers has five neurons. The number of neurons in each hidden layer
is not fixed, and can be changed independently to improve the
accuracy of the estimated attractiveness score produced by the MLP.
The output layer has only one output node, which corresponds to the
estimated attractiveness score in box B3.
[0045] Before the MLP can be used to estimate the attractiveness
score, the MLP must be trained by a group of data called a training
set. The training set is composed of sets of the geometrical
features of facial images of different individuals and
corresponding attractive scores assigned to each of the facial
images. These scores can be obtained from polls or surveys by
asking many people to assign scores to the facial images provided.
Each of the geometrical features of the training set is then fed as
the input to the MLP at the input layer while the corresponding
attractive score is provided to the MLP as the desired output at
the output layer. One by one, the MLP learns to produce the desired
output from the provided input by systematically changing weights
in the neurons in both hidden layers. The learning process of the
MLP can be accomplished by using either the conventional
back-propagation algorithm or the well-known genetic algorithm.
Once the MLP has been well trained to reproduce the correct
corresponding attractive score at the output layer whenever there
is a set of geometrical features of a facial image presented at the
input layer. That MLP can now be used to give an estimated number
or score of the degree of attractiveness of any facial image.
OPERATION
[0046] In operation the user stands between the camera 11 and the
background curtain 18. After the user inserts enough money into the
coin/bill receptor 17, the machine displays a greeting message on
the computer screen 13 and sounds a greeting voice through the
small speaker 14 while lowers the background curtain 18. Next, the
machine turns on the lighting means 12 and directs the user to
position his/her head before pressing the select button 16 to take
his/her facial image. The machine counts down from 3 to 1 and takes
the image. Then, the machine asks the user whether he/she would
like to retake the image. If not, the machine proceeds by
displaying the categories of celebrities such as movie stars,
singers, sports stars, politicians, etc. The user uses the track
ball 15 and the select button 16 to choose a celebrity category.
After the user has selected the celebrity category, the machine
automatically extracts the geometrical features of the user's
facial image and compares it to the entire collection images in the
category to determine a best match. Then, the machine determines
the estimated score of the user's attractiveness. Finally, the
machine displays the matching result and the score to the user on
the computer screen 13.
[0047] Whenever there is no one using the machine for a certain
period of time, say five minutes. The machine rolls up the
background curtain 18 by using the curtain controller 28, and
starts to advertise itself by displaying preprogrammed
advertisement video and audio clips on the computer screen 13 and
the main speaker 19, respectively. This advertisement keeps going
until it is stopped when money is sensed at the coin/bill receptor
17.
RAMIFICATIONS
[0048] The method of estimating the degree of attractiveness of the
human face as taught by this invention can be applied as a means
for prescreening applicants for job positions in which the
applicant appearances play an important role, for example, models,
receptionists, and stewardesses.
[0049] The method of estimating the degree of attractiveness of the
human face as taught by this invention can be also used to
determine a perfect face (the face that its geometrical features
yields 100 for the attractiveness score). This perfect face can
then be used as an outline for creating very appealing human
characters in cartoon or animation movies.
* * * * *