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Real-Time ImplementationFace-Based Auto-Focus

on TI Digital Camera Proces

N. Kehtarnavaz, M. Gamadia, and MSignal and Image Processing

University of Texas at Dalla

TIDCFeb 28, 2008

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Agenda

Overview of passive auto-focus

and previous related projects

Face-based AF feature

Existing approaches

Real-time face detection

DM350 implementation

Demo/Q&A

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Digital Camera Image Pip

Digital camera image pipeline co

many image processing componSome key components are show

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Passive Auto-Focus (A

Extract a measure of sharpness f

Establish a feedback loop to reacin-focus position

Out-of-focus

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Rule-based AF Search

In a previous work (Kehtarnavaz

AF search named Rule-based Sewas developed achieving faster ftime with comparable accuracy tstandard Global Search (GS)

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Rule-based AF Search

Commercial vs. Developed Solut

(DSC class: 10Mpix, 3x zoom)[http://www.dpreview.com]

Vendor Model AF Time

Wide Tele

Canon PowerShot SD900 0.60 0.82

Casio Exilim EX-Z1000 0.46 0.70

Kodak EasyShare V1003 0.69 2.06

Samsung NV10 0.60 0.60

Sony Cyber-Shot DSC-N2 0.29 0.69

UTD TI DM350 based DSC 0.21 0.33

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Multi-Window AF

In a follow-up work (Peddigari 0

extended to multi-windows whicfocusing on objects appearing inparts of the image, thus supportidifferent photography situations

Out of focus In foc

Single Window Multi-Wi

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Low-Light AFRecently (Gamadia 07), a systematic

preprocessing approach has been intenable focusing in low light conditionLow-Light (~30 lux)

Out of focus In focu

Pre-processing

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AF VideoclipsNote: Videoclips shown correspond to the p

resolution), not the capture (high resolution

Example 1: GS vs. RS Global search AF

Multi-win, rule-based AF

Example 2: Single vs. Multi-Window Single-win, rule-based AF

Multi-win, rule-based AFExample 3: Continuous AF

Off

On, Sharpness

Example 4: Low Light AF (16 lux) Preprocessing Off

Preprocessing On

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This Project: Face-based

Objective: Perform AF on faces (ob

interest in great majority of photogr

Solution is software-based, no dedicated pr

Development of a computationally efficient algorireal-time deployment on DM350 or similar camer

Solution to be relatively robust to face rotatidifferent lighting conditions

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Existing Approaches (1

Nearly all major digital camera manincluding Canon, Fuji, Nikon, PanaPentax, Samsung and Sony offer cmodels with face recognition featur

Difference here is to have a softwaapproach for achieving a robust fac

auto-focusing, i.e., without utilizing dedicated face detection hardware

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Existing Approaches (2

The image processing literature include

face detection algorithms based on faciaskin color, face shape, etc.

Although some of these algorithms have

reported to be capable of achieving highrates, very few of them are suitable for rsoftware deployment on digital or cell-p

camera processors due to their high comand memory demands.

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Existing Approaches: Facial F

Facial features consisting of eyes

mouth have been used for face de(e.g., Yow 97)

Pros

High accuracy

Cons

Require access to the entire frontal profile or partial faces

Fail if some portion of face is obstruparticular if eyes are covered (glass

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Existing Approaches: Rule-b

Multi-resolution rule-based face d

utilizing simple rules including posrelative distances between facial f

(e.g.,Yang 02)

Pros

Several different rules can be used

Cons

Very much dependent on the strictn

strict rules fail to detect faces and torules generate many false positives

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Existing Approaches: Skin C

Human skin color is shown to be

feature for performing real-time fadetection (e.g., Paschalakis 04)

Pros

Able to detect profile or partial faces

Able to detect even if some portion obstructed (for example, a person wsunglasses)

Cons

Additional post processing is needeskin areas are also detected

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Our Solution: Also Skin Colo

Although different people have diffcolors, several studies have shownchrominances form a tight color clu

Considered two models to describcluster:

Single Gaussian Model (SGM)

Gaussian Mixture Model (GMM)

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Color Space

Different chrominance spaces can

representing skin color, e.g.: Normalized RGB (r,g)

HSI

YCbCr

Normalized YCbCr

Since raw Bayer pattern images caan image sensor are transformed inYCbCr color space for compressionwithin a digital camera, YCbCr was

here to provide the chrominance in(Normalized YCbCr provided simila

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Model TrainingVarious skin areas from a face im

selected and the corresponding pimapped into the CbCr space.

Y

Cb

Cr

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Face Database (1)

AR Database

Name:Aleix Martinez and Robert Benavente in the Comp

Center (CVC)

Color Images: Yes

Image Format: RAW

Image Size: 768x576

Number of unique people: 126

Number of pictures per

person:26

Number of background

pictures per person:0

Different Conditions: different facial expressions, illumination conditionocclusions (sun glasses and scarf)

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Face Database (2)

PIE DatabaseName: PIE Database

Color Images: Yes

Image Format: JPEG

Image Size: 640 x 486

Number ofunique people: 68

Number of

pictures per

person:

603 (approximately)

Number of

background

pictures per

person:

13

Different

Conditions:

13 different poses, 43 different illumination

conditions, and with 4 different expressions.

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Face Database (3)

UOPB Database

Name: The University of Oulu Physics-Based FaceDatabase

Color Images: Yes

Image Format: BMP

Image Size: 428 x 569

Number of unique

people:125

Number of pictures

per person:16

Number of

background pictures

per person:

0

Different

Conditions:

All frontal images: 16 different camera

calibration and illuminations

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Skin Color Distribution (

0 50 100 150 200 250

50

100

150

200

250

Cb

Cr

Distribution of human skin color wchrominance CbCr space for 250corresponding to the AR, PIE, andatabases

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Skin Color Distribution (2

Distribution of human skin color w

chrominance CbCr space for 30 faces collected by the DM350 pla

0 50 100 150 200 2500

50

100

150

200

250

Cb

Cr

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Skin Color Distribution (3

Example: Skin color distribution cor

to the four manually selected skin a

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SGM(1)

[ ] 1 ,, = = skin skini i

Cb Cr i i

Cb Cr n

1

( )( )

= x x

skin skin t

i iin

, = xskin skin skin

i i iCb Cr

The skin color distribution can be repres

following single Gaussian model N(, )

SGM is then used to construct a binary imrepresenting the skin color pixels of an inpwithin the 98% confidence area in terms o

Mahalanobis distance between the input chrominance pixels and the SGM model.

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SGM (2)

Pros

Fast detection with very low computacomplexity

Cons

Sensitive to the confidence level, high

confidence captures all skin pixels buincreases false alarms

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GMM (1)

The skin color distribution can be more

modeled by a weighted combination of Mdensity functions given by:

A study has shown that the use of two G(M=2) is adequate (Caetanoa 03)

1

( ) ( | ) ( )=

= M

j

p x p x j P j

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GMM (2)

1( | )

( ) ==

N

inew i

P j xP j

n

1

1

=

=

=

n

new ij n

i

x

1

1

[ ].[ ] ( | )

( | )

=

=

=

nnew new T

i j i j inew i

j n

i

i

x x P j x

P j x

The training process is done by us

Expectation Maximization (EM) me

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GMM (3)

Pros

Higher detection accuracy

Cons

Higher computational complexity

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Binary skin images generateSGM and GMM

GMM captures skin area more effectively

compared to SGM, more accurate mode

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GMM Time Issue

GMM increases the skin detection

performance at the expense of muccomputational time.

Floating-point calculation for the Gdensity functions adds to the comptime.

Face detection time with two Gausmixture model: 5 to 6 seconds, not

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Lookup Table (1)

To overcome the time issue, calcu

probability for all possible Cb-Cr coand created a lookup table.

As the variation of skin color in thechrominance space is small as comthe entire color space, it was found50X50 lookup table was adequate fthe skin color cluster.

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Lookup Table (2)

Using the lookup table approach, t

detection using GMM took 10 to 25DM350, considered to be a real-timthroughput as it added an acceptabincrease to passive AF running at 2

ms.

Note that as size of the lookup tablincreasing the number of Gaussianalter this time.

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Lookup Table for GMM

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Post Processing

It is not possible to detect faces us

skin color information because of oexposed skin areas, and also due tpresence of similar colors in the ba

Need a fast post processing step, u

Blocks or so called paxels to reduce dprocessed

Simple shape processing

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Paxel Image (1)

The binary skin image is divided in

or paxels.

Total skin area within a paxel is coif this is greater than or equal to 50

paxel area, the paxel label is assignskin.

This step significantly reduces the

data to be processed, speeding up processing.

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Paxel Image (2)

Example: A 640X480 size skin ima

converted to 64X48 size paxel imagusing a paxel size of 10X10.

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Simple Face Shape ProcesConnectivity Candidate regions are first selected by examinin

neighborhood connectivity among the paxels

Face size A minimum face size of 40X40 pixels in a 640X4

used to obtain candidate face regions (determinROC curve analysis)

Aspect ratio of face Standard golden ratio 1.618 (Yang 02) works w

frontal faces

To accommodate for rotated faces, a more flexihere (aspect ratio between 0.8-1.8) to detect fac

Face score Best face region is then obtained using face sco

amount of skin presence in the face regions

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Shape Processing Examp

Candidate regions after connectivity

Face regions after face size + aspect raBest face region after scoring

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Sample outcome of skin cface detection algorith

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Real-Time DM350 Implement

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Real-Time DM350 Implementa

Extensive experimentation (200 scenes88% of cases, the best (closest) face ardetected and focusing was done on that

Remaining 12% of cases

8% of cases, hands or other exposed parts were picked note: objective here was not rather auto-focusing, therefore these cases these skin areas were on the same focal pla

4% of cases, could not find a well-defined s

associated with a detected face area autoswitched to the original AF using the paxel ano face but having the best sharpness funct

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Face-based AF Videoclip

Single and multiple faces

Frontal faceProfile face

Multiple faces

Different lighting conditions

Fluorescent light

Incandescent light

Mixed light (fluorescent+ incandescen

Outdoor

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Summary

For the last four years, an R&D p

has been established between that UTD and TI to look into variouimprovements of digital and cell-camera image pipelines. This eff

been the latest accomplished prothis program.

Introduced a real-time software bsolution to achieving faced-baseauto-focusing.

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Demo/Q&A

N. Kehtarnavaz, M. Gamadia, and M

Signal and Image Processing

University of Texas at Dalla

Real-Time ImplementationFace-Based Auto-Focus

on TI Digital Camera Proces

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I M P O R T A N T N O T I C E

T e x a s I n s t r u m e n t s I n c o r p o r a t e d a n d i t s s u b s i d i a r i e s ( T I ) r e s e r v e t h e r i g h t t o m a k e c o r r e c t i o n s , m o d i f i c a t i o n s , e n h a n c e m e n t s , i m p r o v e m e n t s , a n d o t h e r c h a n g e s t o i t s p r o d u c t s a n d s e r v i c e s a t a n y t i m e a n d t o d i s c o n t i n u e a n y p r o d u c t o r s e r v i c e w i t h o u t n o t i c e . C u s t o m e r s s h o u l do b t a i n t h e l a t e s t r e l e v a n t i n f o r m a t i o n b e f o r e p l a c i n g o r d e r s a n d s h o u l d v e r i f y t h a t s u c h i n f o r m a t i o n i s c u r r e n t a n d c o m p l e t e . A l l p r o d u c t s a r e s o l d s u b j e c t t o T I s t e r m s a n d c o n d i t i o n s o f s a l e s u p p l i e d a t t h e t i m e o f o r d e r a c k n o w l e d g m e n t .

T I w a r r a n t s p e r f o r m a n c e o f i t s h a r d w a r e p r o d u c t s t o t h e s p e c i f i c a t i o n s a p p l i c a b l e a t t h e t i m e o f s a l e i n a c c o r d a n c e w i t h T I s s t a n d a r d

w a r r a n t y . T e s t i n g a n d o t h e r q u a l i t y c o n t r o l t e c h n i q u e s a r e u s e d t o t h e e x t e n t T I d e e m s n e c e s s a r y t o s u p p o r t t h i s w a r r a n t y . E x c e p t w h e r e m a n d a t e d b y g o v e r n m e n t r e q u i r e m e n t s , t e s t i n g o f a l l p a r a m e t e r s o f e a c h p r o d u c t i s n o t n e c e s s a r i l y p e r f o r m e d .

T I a s s u m e s n o l i a b i l i t y f o r a p p l i c a t i o n s a s s i s t a n c e o r c u s t o m e r p r o d u c t d e s i g n . C u s t o m e r s a r e r e s p o n s i b l e f o r t h e i r p r o d u c t s a n d a p p l i c a t i o n s u s i n g T I c o m p o n e n t s . T o m i n i m i z e t h e r i s k s a s s o c i a t e d w i t h c u s t o m e r p r o d u c t s a n d a p p l i c a t i o n s , c u s t o m e r s s h o u l d p r o v i d e a d e q u a t e d e s i g n a n d o p e r a t i n g s a f e g u a r d s .

T I d o e s n o t w a r r a n t o r r e p r e s e n t t h a t a n y l i c e n s e , e i t h e r e x p r e s s o r i m p l i e d , i s g r a n t e d u n d e r a n y T I p a t e n t r i g h t , c o p y r i g h t , m a s k w o r k r i g h t , o r o t h e r T I i n t e l l e c t u a l p r o p e r t y r i g h t r e l a t i n g t o a n y c o m b i n a t i o n , m a c h i n e , o r p r o c e s s i n w h i c h T I p r o d u c t s o r s e r v i c e s a r e u s e d . I n f o r m a t i o np u b l i s h e d b y T I r e g a r d i n g t h i r d - p a r t y p r o d u c t s o r s e r v i c e s d o e s n o t c o n s t i t u t e a l i c e n s e f r o m T I t o u s e s u c h p r o d u c t s o r s e r v i c e s o r a w a r r a n t y o r e n d o r s e m e n t t h e r e o f . U s e o f s u c h i n f o r m a t i o n m a y r e q u i r e a l i c e n s e f r o m a t h i r d p a r t y u n d e r t h e p a t e n t s o r o t h e r i n t e l l e c t u a l p r o p e r t y o f t h e t h i r d p a r t y , o r a l i c e n s e f r o m T I u n d e r t h e p a t e n t s o r o t h e r i n t e l l e c t u a l p r o p e r t y o f T I .

R e p r o d u c t i o n o f T I i n f o r m a t i o n i n T I d a t a b o o k s o r d a t a s h e e t s i s p e r m i s s i b l e o n l y i f r e p r o d u c t i o n i s w i t h o u t a l t e r a t i o n a n d i s a c c o m p a n i e d b y a l l a s s o c i a t e d w a r r a n t i e s , c o n d i t i o n s , l i m i t a t i o n s , a n d n o t i c e s . R e p r o d u c t i o n o f t h i s i n f o r m a t i o n w i t h a l t e r a t i o n i s a n u n f a i r a n d d e c e p t i v e b u s i n e s s p r a c t i c e . T I i s n o t r e s p o n s i b l e o r l i a b l e f o r s u c h a l t e r e d d o c u m e n t a t i o n . I n f o r m a t i o n o f t h i r d p a r t i e s m a y b e s u b j e c t t o a d d i t i o n a l r e s t r i c t i o n s .

R e s a l e o f T I p r o d u c t s o r s e r v i c e s w i t h s t a t e m e n t s d i f f e r e n t f r o m o r b e y o n d t h e p a r a m e t e r s s t a t e d b y T I f o r t h a t p r o d u c t o r s e r v i c e v o i d s a l l

e x p r e s s a n d a n y i m p l i e d w a r r a n t i e s f o r t h e a s s o c i a t e d T I p r o d u c t o r s e r v i c e a n d i s a n u n f a i r a n d d e c e p t i v e b u s i n e s s p r a c t i c e . T I i s n o t r e s p o n s i b l e o r l i a b l e f o r a n y s u c h s t a t e m e n t s .

T I p r o d u c t s a r e n o t a u t h o r i z e d f o r u s e i n s a f e t y - c r i t i c a l a p p l i c a t i o n s ( s u c h a s l i f e s u p p o r t ) w h e r e a f a i l u r e o f t h e T I p r o d u c t w o u l d r e a s o n a b l y b e e x p e c t e d t o c a u s e s e v e r e p e r s o n a l i n j u r y o r d e a t h , u n l e s s o f f i c e r s o f t h e p a r t i e s h a v e e x e c u t e d a n a g r e e m e n t s p e c i f i c a l l y g o v e r n i n g s u c h u s e . B u y e r s r e p r e s e n t t h a t t h e y h a v e a l l n e c e s s a r y e x p e r t i s e i n t h e s a f e t y a n d r e g u l a t o r y r a m i f i c a t i o n s o f t h e i r a p p l i c a t i o n s , a n d a c k n o w l e d g e a n d a g r e e t h a t t h e y a r e s o l e l y r e s p o n s i b l e f o r a l l l e g a l , r e g u l a t o r y a n d s a f e t y - r e l a t e d r e q u i r e m e n t s c o n c e r n i n g t h e i r p r o d u c t s a n d a n y u s e o f T I p r o d u c t s i n s u c h s a f e t y - c r i t i c a l a p p l i c a t i o n s , n o t w i t h s t a n d i n g a n y a p p l i c a t i o n s - r e l a t e d i n f o r m a t i o n o r s u p p o r t t h a t m a y b e p r o v i d e d b y T I . F u r t h e r , B u y e r s m u s t f u l l y i n d e m n i f y T I a n d i t s r e p r e s e n t a t i v e s a g a i n s t a n y d a m a g e s a r i s i n g o u t o f t h e u s e o f T I p r o d u c t s i n s u c h s a f e t y - c r i t i c a l a p p l i c a t i o n s .

T I p r o d u c t s a r e n e i t h e r d e s i g n e d n o r i n t e n d e d f o r u s e i n m i l i t a r y / a e r o s p a c e a p p l i c a t i o n s o r e n v i r o n m e n t s u n l e s s t h e T I p r o d u c t s a r e s p e c i f i c a l l y d e s i g n a t e d b y T I a s m i l i t a r y - g r a d e o r " e n h a n c e d p l a s t i c . " O n l y p r o d u c t s d e s i g n a t e d b y T I a s m i l i t a r y - g r a d e m e e t m i l i t a r y s p e c i f i c a t i o n s . B u y e r s a c k n o w l e d g e a n d a g r e e t h a t a n y s u c h u s e o f T I p r o d u c t s w h i c h T I h a s n o t d e s i g n a t e d a s m i l i t a r y - g r a d e i s s o l e l y a t t h e B u y e r ' s r i s k , a n d t h a t t h e y a r e s o l e l y r e s p o n s i b l e f o r c o m p l i a n c e w i t h a l l l e g a l a n d r e g u l a t o r y r e q u i r e m e n t s i n c o n n e c t i o n w i t h s u c h u s e .

T I p r o d u c t s a r e n e i t h e r d e s i g n e d n o r i n t e n d e d f o r u s e i n a u t o m o t i v e a p p l i c a t i o n s o r e n v i r o n m e n t s u n l e s s t h e s p e c i f i c T I p r o d u c t s a r e d e s i g n a t e d b y T I a s c o m p l i a n t w i t h I S O / T S 1 6 9 4 9 r e q u i r e m e n t s . B u y e r s a c k n o w l e d g e a n d a g r e e t h a t , i f t h e y u s e a n y n o n - d e s i g n a t e d p r o d u c t s i n a u t o m o t i v e a p p l i c a t i o n s , T I w i l l n o t b e r e s p o n s i b l e f o r a n y f a i l u r e t o m e e t s u c h r e q u i r e m e n t s .

F o l l o w i n g a r e U R L s w h e r e y o u c a n o b t a i n i n f o r m a t i o n o n o t h e r T e x a s I n s t r u m e n t s p r o d u c t s a n d a p p l i c a t i o n s o l u t i o n s :

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