Mathematical Model of Skin

Color for Face Detection

Setiawan Hadi, Adang Suwandi A,

Iping Supriana S, Farid Wazdi

Universitas Padjadjaran, Bandung, Indonesia

Institut Teknologi Bandung, Indonesia

Introduction

• Face detection is a preprocessing step of facial recognition system (Essential)

Introduction

• Goal: localize face(s) in digital image and/or in real time video

Skin-based Face Detection

• Skin important element in detecting image that contain skin or skin-like region

• Skin is special

– covers most of the face image area

– skin of different people appears to vary over a wide

range, however the differ is much less in colour

(chromaticity) than brightness

– detection of skin area in digital image are more

practical and easy to implement.

Our Research Approach

• Skin colour is represented in 3 colour space (rg, HSB and YCbCr)

• Using mathematical model that is generated from face images

• Implement morphological filters for enhancing face image

• Apply 4-neigbourhood ellipse representation for localizing face

• Using local face databases for experiment

Face in Colour Spaces RGB space

HSB space

YCbCr space

Generating Face Skin Model

• Calculate mean and covariance chromaticity of training images for each colour space

• Training images are prepared semi-manually

M k =

n X

i = 1

1

± i T i

M k = 1

± 1

T 1 + 1

± 2

T 2 + ¢ ¢ ¢ + 1

± n ¡ 1

T n ¡ 1 + 1

± n

T n

Sample of generated skin colour model

Skin distribution in Colour Spaces

Face Detection

Algorithm

w h e r e P s k i n ( i ; j ) i s p r o b a b i l i t y o f

p i x e l P a s s k i n p i x e l i f i n c l u d e d

i n d i s t r i b u t i o n s k i n m o d e l D M k

f o r e v e r y c o l o u r s p a c e s R n .

P s k i n ( i ; j ) = P s k i n ( i ; j ) 2 D M k 8 P ( i ; j ) ^ 8 R n

Visual Result

Experiments

Concluding remarks

• Skin colour is modelled using using mean-covariance characteristics

• Skin colour is represented in 3 colour space

• Skin model is used for face detection, with support morphological filter dan 4-neigborhood ellipse generation

• Experiment has been performed using 7 sets of face database, >>3000 face images

• Accuracy needs to be improved

Next Work

• Multiple image detection

• Symmetry and features detection

• Adding geometric-based detection to increase detection accuracy

• Algorithm improvement for efficient yet faster detection

• Realtime face detection

• Face recognition module

Mathematical Model of Skin

Color for Face Detection

Setiawan Hadi, Adang Suwandi A,

Iping Supriana S, Farid Wazdi

Universitas Padjadjaran, Bandung, Indonesia

Institut Teknologi Bandung, Indonesia