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[Lab image] Component Analysis
Head: Fernando De la Torre Frade
Contact: Fernando De la Torre Frade (ftorre@cs.cmu.edu)

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

Associated center: VASC

For more information, see this lab's homepage.

Jump to: Lab Description | Personnel | Projects | Publications

Lab Description

The Component Analysis Lab (CAL) started in 2007 and it is a research Lab in the Robotics Institute at Carnegie Mellon University. CAL is devoted to research new learning techniques to encode and decompose a given signal into relevant components for classification, clustering, modeling and visualization. Our research spans a wide range of areas in computer vision, computer graphics, machine learning and robotics, with applications to the fields of human health, biometrics and human-machine interface. For more information see http://ca.cs.cmu.edu/

Personnel [Past members]

Name Title Email Address
Xavier Boix Bosch Visiting Scholar xboix@salle.url.edu
Joan Campoy Robotics Engineer tm08308@salle.url.edu
Dan Casas Visiting Scholar dan.casas@gmail.com
Jeffrey's personal homepage Jeffrey Cohn Adjunct Faculty (Adjunct) jeffcohn@cs.cmu.edu
Fernando's personal homepage Fernando De la Torre Frade Assistant Research Professor ftorre@cs.cmu.edu
Ruben Garcia Researach Associate I garciagarciar@gmail.com
Javier Hernandez Research Associate I javierh@andrew.cmu.edu
Takeo's personal homepage Takeo Kanade U.A. and Helen Whitaker University Prof., RI/CS tk@cs.cmu.edu
Javier Montano Martinez Research Associate I jmontano.84@gmail.com
Minh's personal homepage Minh Hoai Nguyen PhD Student, RI minhhoan@andrew.cmu.edu
Maria Teresa Ortin Visiting Scholar mariate.ortin@gmail.com
Gemma Roig Noguera Visiting Scholar gemmarono@gmail.com
Tomas Simon Kreuz Research Associate tsimon@andrew.cmu.edu
Margara Tejera Research Associate I margaratejera@gmail.com
Sergio Valcarcel Macua Research Associate I sergiov@cmu.edu
Zengyin Zhang Research Associate I zhangzy@cmu.edu
Feng's personal homepage Feng Zhou Research Associate I zhfe99@gmail.com

Current Projects

Deception Detection - Learning facial indicators of deception
Depression Assessment - This project aims to compute quantitative behavioral measures related to depression severity from facial expression, body gestures, and vocal prosody in clinical interviews.
Face Recognition - Recognizing people from images and videos.
Facial Expression Analysis - Automatic facial expression encoding, extraction and recognition, and expression intensity estimation for the applications of MPEG4 application: teleconferencing, human-computer interaction/interface.
Facial Feature Detection - Detecting facial features in images.
Feature Selection - Feature selection in component analysis.
Forecasting the Anterior Cruciate Ligament Rupture Patterns - Use of machine learning techniques to predict the injury pattern of the Anterior Cruciate Ligament (ACL) using non-invasive methods.
Hot Flash Detection - Machine learning algorithms to detect hot flashes in women using physiological measures.
Image Alignment - Image alignment with parameterized appearance models.
Indoor People Localization - Tracking multiple people in indoor environments with the connectivity of Bluetooth devices.
Intelligent Diabetes Assistant - We are working to create an intelligent assistant to help patients and clinicians work together to manage diabetes at a personal and social level. This project uses machine learning to predict the effect that patient specific behaviors have on blood glucose.
Learning Optimal Representations - Learning optimal representations for classification, image alignment, visualization and clustering.
Low Dimensional Embeddings - Finding low dimensional embeddings of signals for optimal modeling, classification and clustering.
Multimodal Diaries - Summarization of daily activity from multimodal data (audio, video, body sensors and computer monitoring)
Temporal Segmentation of Human Motion - Temporal segmentation of human motion
Unification of Component Analysis - This project aims to find the fundamental set of equations that unifies all component analysis methods.

Recent publications [View all 100 publications]

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