Normal agallagh 19 5336 2006-06-12T16:03:00Z 2008-03-11T20:52:00Z 1 1019 5813 Carnegie Mellon University 48 13 6819 10.3501

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Group Member

Andrew Gallagher
Ph.D. Student

Personal Homepage:
Email: agallagh@

Office:    Porter Hall B23
Phone:  412-268-4464
Fax:       412-268-1679

Mailing Address:
Department of ECE,
Carnegie Mellon University ,
5000 Forbes Avenue , Pittsburgh ,
PA 15213-3890

[Biography]  [Research Interests] [Project] [Publications] [Patents


I am a Ph.D. student in the Electrical and Computer Engineering Department of Carnegie Mellon University. I’m supervised by Professor Tsuhan Chen.  In addition to being a doctoral student, I am also a senior principal scientist in the Intelligent Systems Research Center at the Eastman Kodak Company. I successfully defended in May 2009.

My educational background is in electrical engineering, signal processing, and machine learning.  I have earned a B.S. degree in Electrical Engineering from Geneva College and an M.S. degree in Electrical Engineering from the Rochester Institute of Technology. 

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Research Interests

Using Context to Recognize People in Consumer Images 
Consumers are amassing large collections of digital images and videos. Consumers will want the ability to search their collections based on the identity of who is in the image. In my research, I am exploring ways of understanding and recognizing people based on the fusion between images and context.

What is context? Context is data that helps us draw conclusions about the identities of people in the image. Traditionally, faces are used to identify people. We know the time and location of the image can aid in recognition. Context can include social factors such as the first names of people in an image (first names rise and fall in popularity and contain information about the age and gender of the person.)


I use probabilistic models and machine learning to integrate context into the interpretation of people in images.


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Jointly Estimating Demographics and Height with a Calibrated Camera
In this paper, we propose that measuring people and estimating demographics are problems that should be solved jointly. We use anthropometric statistics (i.e. statistics on measurements of the human body) with a calibrated camera to provide accurate height measurements and infer gender and age too.

Understanding Images of Groups of People
One common image is the "Group Shot" where several family members or friends appear together in an image. In this paper, we study the spatial juxtaposition of faces in an image. We introduce features that encapsulate the structure of the group. These features provide useful context for demographic recognition, find images of groups eating, and even locate the horizon!

Estimating Age, Gender, and Identity using First Name Priors
People have an amazing ability to interpret still images of people. We innately put our lifetime of experience in social situations to use for interpreting images. We want to give computer vision algorithms this same social context. In this paper, we merge demographic data related to first names in the United States with gender and age classifiers to identify people in images based on their names alone.  

Clothing Cosegmentation for Recognizing People 
Consumer photographers usually capture multiple images during a single event. Because people's clothing does not generally change during an event, clothing can help identify people (sometimes more reliably than faces!) The challenge is to achieve accurate clothing segmentation. In this paper we explore the relationship between clothing segmentation and person recognition. Also, we introduce the Gallagher Collection Person Dataset for other researchers to use.  

Image Authentication by Detecting Traces of Demosaicing
How can we distinguish real images (from a camera) from fakes (either computer-generated or constructed by compositing parts of other images)? Images from a digital camera contain traces of demosaicing (the process used to construct a full-color image from an image sensor with a color filter array). We demonstrate an elegant approach for locally detecting demosaicing. We show the best yet performance at distinguishing real photographs from computer graphics on an established test set. And we show accurate localization of forged image regions in tampered images.

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Journal Papers: 


Conference Papers: 

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