J Hopkins 3oct03

“15 SECONDS OF FAME”Use of Computer Vision in a Modern Art Installation: 

“15 SECONDS OF FAME” Use of Computer Vision in a Modern Art Installation Franc Solina Computer Vision Laboratory Faculty of Computer and Information Science University of Ljubljana, Slovenia

Motivation for this work: 

Motivation for this work collaboration with the Academy of Fine Arts in Ljubljana since 1995 new media, computer-based art installations (internet, virtual galleries, video, mobile robots, remote operation) work of scientist and conceptual artist Ken Goldberg, UC Berkeley (TELEGARDEN) COMPUTER VISION + ART INSTALLATION = ?

Video cameras in art installations: 

Video cameras in art installations wooden mirror (Daniel Rozin) touch me (Alba d’Urbano) liquid views (Monika Fleischman) … TECHNICAL LIMITATIONS: precise positioning of the subject

“In the future everybody will be famous for 15 minutes.”Andy Warhol: 

“In the future everybody will be famous for 15 minutes.” Andy Warhol Marilyn Monroe (Andy Warhol, 1964)

Image mediated culture: 

Image mediated culture people like to look at themselves (mirrors, photos, paintings, video) vanity, self-discovery, self-assertion a face in mass culture -> FAME media attention - a mirror of the indivudual’s self-perception WARHOL: celebrity photo -> portrait warhol-like portrait -> instant celebrity

Faces in computer vision: 

Faces in computer vision images of people find people, identify them, determine their activity video surveillance face recognition <- FACE DETECTION

15 seconds of fame: 

15 seconds of fame

Hardware: 

Hardware Digital camera LCD monitor computer USB

Software: 

Software input photo transformation color filters pop-art portrait illumination compensation find faces + randomly select one

Roadmap: 

Roadmap color-based face detection illumination compensation pop-art color transformations display and ordering of portraits over the Internet conclusions

Our original face detection: 

Our original face detection

Simplified face detection 1: 

Simplified face detection 1

Simplified face detection 2: 

Simplified face detection 2 ADVANTAGES: faster, detected also faces from profile DISADVANTAGES: faces of dark complexion not detected, other body parts can be detected

Eliminating the influence of non-standard illumination: 

Eliminating the influence of non-standard illumination different from daylight illumination color constancy/compensation methods eestimate the present illumination reconstruct the image under standard illumination run face detection algorithm

Color compensation methods: 

Color compensation methods close to standard illumination low time complexity Grey World Average surface color in the image is achromatic Illumination estimation: average color Mean gray value Modified Grey World Illumination estimation: each color is counted only once White-Patch Retinex On each image white surface is present Illumination estimation: maximal color

Color compensation methods: 

Color compensation methods NO GW MGW RET NO – original GW – Gray World MGW – Modified GW RET – White-Patch Retinex

Color constancy methods: 

Color constancy methods far from standard illumination Color by Correlation (1) LEARNING: Take images of the Macbeth color checker under present illum. and under standard illum. Use correlation to compute the transform. Parameters (2) APPLY TRANSFORMATION

Color comp. + correll. method: 

Color comp. + correll. method NO GW MGW RET COR NO – original GW – Gray World MGW – Modified GW RET – White-Patch Retinex COR – Color by Correlation

Face detection results #1: 

Face detection results #1

Face detection after GW : 

Face detection after GW GW

Face detection results #2: 

Face detection results #2

Face detection after COR: 

Face detection after COR COR

Warhol’s celebrity portraits: 

Warhol’s celebrity portraits segment the face from the background delineate the contours highlight some facial features (mouth, eyes, hair) overlay with color screens above transformations -> shape grammar BUT: requires automatic segmentation into constituent face parts

pop-art color filters: 

pop-art color filters color-balance random coloring posterize hue-saturation color-balance posterize hue-saturation 17 universal filters

Display of portraits: 

Display of portraits 4 smaller portraits same filter different configurations 1 big portrait each with a different filter horizontal flip each time a different person no detection -> last detected face with a different pop-art filter 15 second counter

E-mail ordering of portraits: 

E-mail ordering of portraits Ordering system Beside the portrait is displayed an unique ID number Sending e-mail to 15sec@lrv.fri.uni-lj.si Sending the requested picture Creating of the web page

The gallery of “famous” people: 

The gallery of “famous” people from the project web page: black.fri.uni-lj.si/15sec

Audience interactions: 

Audience interactions people quickly realize that portraits of people present at the moment are displayed if several people are present, becoming famous is elusive subtle staging to get one’s most favourable image on the screen subdued competition for “media” attention narcissistic and voyeristic use of the “electronic mirror”

Exhibitions in art galleries: 

Exhibitions in art galleries Forum Stadtpark, Graz, Austria, 19-26 Sep. 2003 Finzgar Gallery, Ljubljana, 14-26 Nov. 2002 8th International Festival of Computer Arts, Maribor, 28 May-1 June 2002

Conclusions: 

Conclusions well accepted by the audience no visible interface a group of people can interact at once exact positioning of observers not necessary at least one face should be found in the input image -> high percentage of true positive face detections -> percentage of true negative face detections can be low a huge database for testing face detection is generated The goal was not to mimic Andy Warhol’s portraits per se but to play upon the celebrification process and the discourse taking place in front of the installation.

From the first public showing: 

From the first public showing