identification of mpeg-4 fdp patterns in human faces using data-mining techniques work subsidized by...
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Identification of MPEG-4 FDP Patterns in Human Faces using Data-Mining Techniques
Work subsidized by projects: HUMODAN IST-2001-32202 • CICYT TIC2001-0931 • TIC2002-10743-E
Britos, P. [email protected]
Perales López, F. [email protected]
Abásolo, M.J. [email protected]
García Martínez, R. [email protected]
xy
z
11.5
11.4
11.2
10.2
10.4
10.10
10.810.6
2.14
7.1
11.6 4.6
4.4
4.2
5.2
5.4
2.10
2.122.1
11.1
Tongue
6.26.4 6.3
6.1Mouth
8.18.9 8.10 8.5
8.3
8.7
8.2
8.8
8.48.6
2.2
2.3
2.6
2.82.9
2.72.5 2.4
2.12.12 2.11
2.142.10
2.13
10.610.8
10.4
10.2
10.105.4
5.2
5.3
5.1
10.1
10.910.3
10.510.7
4.1 4.34.54.6
4.4 4.2
11.111.2 11.3
11.4
11.5
x
y
z
Nose
9.6 9.7
9.14 9.13
9.12
9.2
9.4 9.15 9.5
9.3
9.1
Teeth
9.10 9.11
9.8
9.9
Right eye Left eye
3.13
3.7
3.9
3.5
3.1
3.3
3.11
3.14
3.10
3.12 3.6
3.4
3.23.8
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 face.
MPEG-4 FDPs
3.23.63.4
3.8 3.11
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.
C5.0 vs. C4.5More 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.
Cluster vs. entire databaseMore precise rules by analysing the main cluster instead of the whole database.
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
Rules obtained with C5.0•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
Discretization of the continuous fields allows using it as an objective of the rules.
FIELD DISCRETIZATIONSOM are used to
classify high- dimensional data. In this work we use SOM for clustering the records.
SELFORGANIZING
MAPS
DATABASE OF FACES
FH: 11.1 to 2.1
FW: 10.10 to 10.9
In our work a face is described by distances between different MPEG-4 FDPs. (i.e. mouth width, eyebrown width, etc.). We have a database of 600 faces of different sex, race, etc.
RE: 4.2 to 4.6
REW: 3.12 to 3.8REH: 3.14 to 3.10
NH: 9.6 to 9.2
NT: 9.3 to 9.15MW: 8.4 to 8.3
MH: 8.1 to 8.2
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.)
Example: objective field “sex”
Rules obtained with C4.5•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•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
C4.5
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.
C5.0
DATA MINING TECHNIQUES
Data mining is all about extracting patterns from a
warehoused data.