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Component Analysis
Head: Fernando De la Torre Frade
Contact: Fernando De la Torre Frade
Associated center(s) / consortia:
 Vision and Autonomous Systems Center (VASC)
Lab Homepage
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.
Face Recognition Across Illumination
Recognizing people from faces: video and still iamges.
Face Recognition Across Pose
Recognizing people from different poses.
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.
Multimodal Diaries
Summarization of daily activity from multimodal data (audio, video, body sensors and computer monitoring)
Unification of Component Analysis
This project aims to find the fundamental set of equations that unifies all component analysis methods.