Research Themes
Perceptual and cognitive systems
Idiap combines its multi-diciplinary expertise to advance the understanding of human perceptual and cognitive systems, engaging in research on multiple aspects of human-computer interaction with computational artefacts such as natural language understanding and translation, document and text processing, vision and scene analysis, multimodal interaction, computational cognitive systems, and methods for automatically training such systems (see our research efforts in machine learning).
Social / human behavior
Social Signal Processing is the domain aimed at automatic understanding of social interactions through analysis of nonverbal behavior
Information interfaces and presentation
Information processing by computers must be accompanied by the capacity to present results to users in an efficient and usable way, using human-computer interfaces. In the case of interactive systems, these interfaces must also allow users to enter information in a simple and reliable way, and in the most advanced cases to acquire information from users in a non-disruptive ways. Current research directions at Idiap focus on multimedia information systems, user interfaces, and the evaluation of interactive systems, explained below in more detail.
Biometric Person Recognition
Conventional means of identification such as passwords, secret codes and personal identification numbers (PINs) can easily be compromised, shared, observed, stolen or forgotten. However, a possible alternative in determining the identities of users is to use biometrics.
Biometric person recognition refers to the process of automatically recognizing a person using distinguishing behavioral patterns (gait, signature, keyboard typing, lip movement, hand-grip) or physiological traits (face, voice, iris, fingerprint, hand geometry, electroencephalogram -- EEG, electrocardiogram -- ECG, ear shape, body odor, body salinity, vascular). Over the last decades, several of these biometric modalities have been investigated (fingerprint, iris, voice, face) and are still under consideration. More recently, novel biometric modalities have emerged (gait, EEG, vascular) mainly due to the development of sensor technologies.
Biometric person recognition offers a wide range of challenging fundamental and concrete problems in image processing, computer vision, pattern recognition and machine learning. It is thus a truly inter-disciplinary research field.
Machine learning
Research in machine learning aims at developing computer programs able to learn from examples. Instead of relying on a careful tuning of parameters by human experts, machine learning techniques use statistical methods to directly estimate the optimal setting, which can hence have a complexity beyond what is achievable by a human experts.
Information Scalable Inference and Decision Systems
Sensors, signal processing hardware, and mathematical algorithms are under increasing pressure to accommodate higher dimensional data sets; ever faster capture, sampling and processing rates; ever lower power consumption; communication over ever more difficult channels; and radically new sensing modalities. Moreover, the need for near real-time action based on the sensed data has rendered automatic learning, understanding, and decision making vital to science, industry, and defense. Fortunately, over the past few decades, there has been an enormous increase in computational power and data storage capacity, which provides a new angle to fundamentally re-examine the theory and practice of data-to-knowledge transition.
This research avenue at Idiap develops new theories, models, algorithms, and tools for optimized information extraction from signals. We are interested in (i) reducing the dimensionality in signals to operate at their intrinsic information rate as much as possible at every stage of the data processing pipeline, (ii) modeling and designing sensing systems with statistical methods, information theoretic metrics and multi-objective optimization, and (iii) learning and exploiting parsimony in signals and systems for efficient prediction and inference.
You can find a list of keywords and contact persons of Idiap's research interests here.
