BIOMAT Database Logo BIOMAT Database Logo

Learning from humans: computational modeling of face recognition. 2005

Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Max Planck Institute for Biological Cybernetics, Tübingen, Germany. christian.wallraven@tuebingen.mpg.de

In this paper, we propose a computational architecture of face recognition based on evidence from cognitive research. Several recent psychophysical experiments have shown that humans process faces by a combination of configural and component information. Using an appearance-based implementation of this architecture based on low-level features and their spatial relations, we were able to model aspects of human performance found in psychophysical studies. Furthermore, results from additional computational recognition experiments show that our framework is able to achieve excellent recognition performance even under large view rotations. Our interdisciplinary study is an example of how results from cognitive research can be used to construct recognition systems with increased performance. Finally, our modeling results also make new experimental predictions that will be tested in further psychophysical studies, thus effectively closing the loop between psychophysical experimentation and computational modeling.

UI MeSH Term Description Entries
D007858 Learning Relatively permanent change in behavior that is the result of past experience or practice. The concept includes the acquisition of knowledge. Phenomenography
D008960 Models, Psychological Theoretical representations that simulate psychological processes and/or social processes. These include the use of mathematical equations, computers, and other electronic equipment. Model, Mental,Model, Psychological,Models, Mental,Models, Psychologic,Psychological Models,Mental Model,Mental Models,Model, Psychologic,Psychologic Model,Psychologic Models,Psychological Model
D010364 Pattern Recognition, Visual Mental process to visually perceive a critical number of facts (the pattern), such as characters, shapes, displays, or designs. Recognition, Visual Pattern,Visual Pattern Recognition
D010775 Photic Stimulation Investigative technique commonly used during ELECTROENCEPHALOGRAPHY in which a series of bright light flashes or visual patterns are used to elicit brain activity. Stimulation, Photic,Visual Stimulation,Photic Stimulations,Stimulation, Visual,Stimulations, Photic,Stimulations, Visual,Visual Stimulations
D011601 Psychophysics The science dealing with the correlation of the physical characteristics of a stimulus, e.g., frequency or intensity, with the response to the stimulus, in order to assess the psychologic factors involved in the relationship. Psychophysic
D003198 Computer Simulation Computer-based representation of physical systems and phenomena such as chemical processes. Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D005145 Face The anterior portion of the head that includes the skin, muscles, and structures of the forehead, eyes, nose, mouth, cheeks, and jaw. Faces
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D016571 Neural Networks, Computer A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming. Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D019540 Area Under Curve A statistical means of summarizing information from a series of measurements on one individual. It is frequently used in clinical pharmacology where the AUC from serum levels can be interpreted as the total uptake of whatever has been administered. As a plot of the concentration of a drug against time, after a single dose of medicine, producing a standard shape curve, it is a means of comparing the bioavailability of the same drug made by different companies. (From Winslade, Dictionary of Clinical Research, 1992) AUC,Area Under Curves,Curve, Area Under,Curves, Area Under,Under Curve, Area,Under Curves, Area

Related Publications

Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Face Recognition by Humans and Machines: Three Fundamental Advances from Deep Learning.
September 2021, Annual review of vision science,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Learning invariant face recognition from examples.
May 2013, Neural networks : the official journal of the International Neural Network Society,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Modeling face recognition in the predictive coding framework: A combined computational modeling and functional imaging study.
November 2023, Cortex; a journal devoted to the study of the nervous system and behavior,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Computational modeling of threat learning reveals links with anxiety and neuroanatomy in humans.
April 2022, eLife,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Computational modeling of epiphany learning.
May 2017, Proceedings of the National Academy of Sciences of the United States of America,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.
December 2017, Vision research,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Learning Compact Binary Face Descriptor for Face Recognition.
October 2015, IEEE transactions on pattern analysis and machine intelligence,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Towards Computational Modeling of Human Goal Recognition.
January 2021, Frontiers in artificial intelligence,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Dual-Proxy Modeling for Masked Face Recognition.
January 2022, Procedia computer science,
Christian Wallraven, and Adrian Schwaninger, and Heinrich H Bülthoff
Boosted online learning for face recognition.
April 2009, IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society,
Copied contents to your clipboard!