Transcript Diapositiva 1

Identification of MPEG-4 FDP Patterns in
Human Faces using Data-Mining Techniques
Abásolo, M.J.
Britos, P.
[email protected] [email protected]
García Martínez, R.
[email protected]
The main purpose of this work is to induce rules that
describe patterns in human faces, that means
relations between different dimensions of a face.
MPEG-4
FDPs
MPEG-4 is an ISO/IEC standard which defines
84 feature points called Face Definition
Parameters (FDPs) to parameterise a face.
FDPs are used to personalize a generic face
model to a particular
11.5
11.5 face.
11.1
4.4
4.2 4.1
4.6
In our work a face is described by distances
11.2
between different
MPEG-411.1
FDPs. (i.e.
mouth width, eyebrown width, etc.). We
4.4
have a database of 600 faces of different
4.2
sex, race, etc.
4.6
11.3
4.3
4.5
11.6
10.2
10.4
10.6
10.1
10.7
10.5
5.1
z
5.4
10.4
10.8
10.6
5.2
y
x
2.13
2.1
2.11
2.12
2.10
z
7.1
2.10
2.14
2.1
DATABASE OF
FACES
10.10
10.3
5.3
2.14
x
10.2
10.9
10.10
5.4
10.8
5.2
y
RULES FOR WHAT?
•To personalize a generic face model with standard
measures according some conditions like sex, race, etc.
•To discover relations between different parts of a face
•To discover relations between the parts of a face and other
characteristics like sex, race, height, etc.
•To classify an unknown face example (sex, race, etc.)
11.4
11.4
11.2
Perales López, F.
[email protected]
2.12
RE: 4.2 to 4.6
REH: 3.13
3.14 to 3.10
3.14
REW: 3.12 to 3.8
3.2
3.1
3.2
3.6
3.6
3.12
3.8
3.8
3.11
3.11
FW: 10.10 to 10.9
NH:
9.6 to 9.2
3.5
3.7
3.3
3.4
3.4
3.9 NT: 9.3 to 9.15
9.6
3.10
Right eye
Left eye
MW:9.7
8.4 to 8.3
MH: 8.1 to 8.2
9.8
Nose
FH: 11.19.12
to 2.1
9.14
9.10
9.13
9.11
9.3
9.9
Teeth
6.4
6.2
6.3
9.1
9.2
Discretization of8.6the 8.9
continuous8.4fields
2.7
2.5allows
using it as an objective of
the rules.
2.9
SELF
ORGANIZING
MAPS
9.5
8.10
8.1
2.2
2.3
FIELD
8.2
Mouth
DISCRETIZATION
8.8
SOM are used to
Tongue
6.1
classify highdimensional data. In
this work we use SOM
for clustering the
records.
9.15
9.4
8.5
2.6
2.8
DATA MINING
TECHNIQUES
Data mining is all about
extracting patterns from a
warehoused data.
8.3
2.4
C4.5
8.7
C5.0
Example: objective field “sex”
Rules obtained with C4.5
Example: objective field discretized“FH”
Rules obtained with C4.5
•IF FW >=5 THEN FH range = 5
•IF LE >= 84mm THEN FH range = 4
•IF LEH < 40mm THEN FH range = 2
•IF NH >= 54.6mm THEN FH range = 1
•IF weight < 50 kg. THEN range = 1
•IF LID >= 25mm THEN FH range = 4
•IF weight < 63 kg. THEN sex = female
•IF NA >= 81,57º THEN sex = female
•IF weight >= 72 kg. THEN sex = male
•IF weight < 72 kg. THEN sex = female
•IF RID >= 23 mm THEN sex = male
Rules obtained with C5.0
C4.5 is an automatic
learning algorithm for
classifying examples.
It obtains decision
trees or sets of if-then
rules forms.
C5.0 is an
improvement of C4.5.
•IF weight < 70 kg. AND LE <=79mm THEN sex = female
•IF weight <= 62 kg. THEN sex = female
•IF weight >= 62 kg. AND LE >79mm THEN sex = male
•IF weight > 70 kg. THEN sex = male
Rules obtained with C5.0
Cluster vs. entire database
More precise rules by analysing
the main cluster instead of the
whole database.
•IF NH <= 53.9mm THEN FH range = 1
•IF LEH <= 55mm AND LE > 71mm AND MH <=21mm THEN FH range = 2
•IF LEH <= 55mm AND LE <= 71mm AND NH > 23.9mm THEN FH range = 2
•IF LEH > 55mm AND LE <= 88mm AND LID < 24mm THEN FH range = 3
•IF LEH <= 55mm AND LE > 71mm AND MH >21mm THEN FH range = 3
•IF LEH > 55mm THEN FH range = 4
Work subsidized by projects: HUMODAN IST-2001-32202 • CICYT
TIC2001-0931 • TIC2002-10743-E
C5.0 vs. C4.5
More precise rules with C5.0 than with C4.5.
More complex rules with C5.0 (left part of the
rule has a conjunction) than with C4.5.