Blog
The latest from Google Research
Hopping on a Face Manifold via People Hopper
miércoles, 3 de marzo de 2010
Posted by Sanjiv Kumar and Henry Rowley, Google Research
A few weeks ago we
announced
the launch of a new orkut application in
Google Labs
called
People Hopper
that lets you take your profile image and "morph" it into a friend's photo, using publicly available images from other orkut users along the way. No computer graphics tricks are used; every image along the transition comes from real orkut users.
The application hops across millions of public user images in orkut so that one image is smoothly transformed into another. First, faces are automatically detected in public profile images and normalized in contrast and size. Then, for each image, we find other public profile images that are similar to it. Finally, when you pick two faces, we just hop between similar public images, step-by-step, until the connection is made.
People Hopper was outcome of the following research question: Is it possible to learn a low-dimensional space (i.e. a manifold) in which all the human face images live? It is well-known in the machine learning community that to recover the true underlying manifold one needs a large number of samples from it. In 2008, we published a
paper
at CVPR in which we learned a face manifold using tens of millions of images, which is still the largest scale manifold learning study to date.
To be able to do manifold learning at such a large scale, we had to address two key issues: First, how to do nearest neighbor search in very large databases? We used
spill-trees
to speed up the search to construct the neighborhood graph. Second, how to do spectral decomposition of matrices which are hundreds of terabytes in size? We investigated
sampling-based matrix decomposition methods
to handle such matrices.
One way to visualize the quality of the manifold is to find shortest paths between pairs of faces in the manifold, and observe the smoothness of the transitions between them. This is exactly what People Hopper does. Curious?
Try People Hopper
on orkut now!
The quality of the face manifold depends on three main factors: the number of faces in the manifold, the appearances of those faces, and the similarity measure used for image matching. Since we cannot control the number or appearance of the faces in orkut profiles, it may happen that for a particular image there exists no visually similar image in the database. We plan to update our graph over public profile images frequently, so the quality of paths will change as users join orkut or update their profile images. Finding better contrast normalization and similarity measures is a topic of continuing research. Currently we don't use any face-specific features during this process, just simple image distances.
We are eager to hear your
feedback
on how we can make this application more fun and useful. Also, if for any reason you would prefer your profile image not to appear in any People Hopper path, you can choose to opt out by visiting our
People Hopper homepage
.
Etiquetas
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
jul
jun
may
abr
mar
feb
ene
2020
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2019
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2018
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2017
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2016
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2015
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2014
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2013
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2012
dic
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2011
dic
nov
sep
ago
jul
jun
may
abr
mar
feb
ene
2010
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2009
dic
nov
ago
jul
jun
may
abr
mar
feb
ene
2008
dic
nov
oct
sep
jul
may
abr
mar
feb
2007
oct
sep
ago
jul
jun
feb
2006
dic
nov
sep
ago
jul
jun
abr
mar
feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
