2011
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Rodriguez, J; Gutierrez-Osuna, R Reverse caricatures effects on three-dimensional facial reconstructions Journal Article In: Image and Vision Computing, vol. 29, no. 5, pp. 329-334, 2011. @article{rodriguez2011reverse,
title = {Reverse caricatures effects on three-dimensional facial reconstructions},
author = {J Rodriguez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rodriguez2011reverse.pdf},
year = {2011},
date = {2011-01-01},
journal = {Image and Vision Computing},
volume = {29},
number = {5},
pages = {329-334},
publisher = {Elsevier},
abstract = {Previous research has shown that familiarization with three-dimensional (3D) caricatures can help improve recognition of same-race and other-race faces, a result that may lead to new training tools in security applications. Since 3D facial scans are not generally available, here we sought to determine whether 3D reconstructions from 2D frontal images could be used for the same purpose. Our results suggest that, despite the high level of photographic realism achieved by current 3D facial reconstruction methods, additional research is needed in order to reduce reconstruction errors and capture the distinctive facial traits of an individual.},
keywords = {Face perception},
pubstate = {published},
tppubtype = {article}
}
Previous research has shown that familiarization with three-dimensional (3D) caricatures can help improve recognition of same-race and other-race faces, a result that may lead to new training tools in security applications. Since 3D facial scans are not generally available, here we sought to determine whether 3D reconstructions from 2D frontal images could be used for the same purpose. Our results suggest that, despite the high level of photographic realism achieved by current 3D facial reconstruction methods, additional research is needed in order to reduce reconstruction errors and capture the distinctive facial traits of an individual. |
2010
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Yu, N Y; Yamauchi, T; Yang, H F; Chen, Y L; Gutierrez-Osuna, R Feature selection for inductive generalization Journal Article In: Cognitive science, vol. 34, no. 8, pp. 1574–1593, 2010. @article{yu2010cogsci,
title = {Feature selection for inductive generalization},
author = {N Y Yu and T Yamauchi and H F Yang and Y L Chen and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/yu2010cogsci.pdf},
year = {2010},
date = {2010-01-01},
journal = {Cognitive science},
volume = {34},
number = {8},
pages = {1574--1593},
publisher = {Wiley Online Library},
abstract = {Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data.},
keywords = {Face perception},
pubstate = {published},
tppubtype = {article}
}
Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data. |
2009
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Rodriguez, J; Bortfeld, H; Rudomin, I; Hernandez, B; Gutierrez-Osuna, R The reverse-caricature effect revisited: Familiarization with frontal facial caricatures improves veridical face recognition Journal Article In: Applied cognitive psychology, vol. 23, no. 5, pp. 733–742, 2009. @article{rodriguez2009reverse,
title = {The reverse-caricature effect revisited: Familiarization with frontal facial caricatures improves veridical face recognition},
author = {J Rodriguez and H Bortfeld and I Rudomin and B Hernandez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rodriguez2009reverse.pdf},
year = {2009},
date = {2009-01-01},
journal = {Applied cognitive psychology},
volume = {23},
number = {5},
pages = {733--742},
publisher = {Wiley Online Library},
abstract = {Prior research suggests that recognition of a person's face can be facilitated by exaggerating the distinctive features of the face during training. We tested if this ‘reverse-caricature effect’ would be robust to procedural variations that created more difficult learning environments. Specifically, we examined whether the effect would emerge with frontal rather than three-quarter views, after very brief exposure to caricatures during the learning phase and after modest rotations of faces during the recognition phase. Results indicate that, even under these difficult training conditions, people are more accurate at recognizing unaltered faces if they are first familiarized with caricatures of the faces, rather than with the unaltered faces. These findings support the development of new training methods to improve face recognition.},
keywords = {Face perception},
pubstate = {published},
tppubtype = {article}
}
Prior research suggests that recognition of a person's face can be facilitated by exaggerating the distinctive features of the face during training. We tested if this ‘reverse-caricature effect’ would be robust to procedural variations that created more difficult learning environments. Specifically, we examined whether the effect would emerge with frontal rather than three-quarter views, after very brief exposure to caricatures during the learning phase and after modest rotations of faces during the recognition phase. Results indicate that, even under these difficult training conditions, people are more accurate at recognizing unaltered faces if they are first familiarized with caricatures of the faces, rather than with the unaltered faces. These findings support the development of new training methods to improve face recognition. |
Yu, N Y; Yamauchi, T; Gutierrez-Osuna, R Similarity perception of visual objects: A machine-learning approach Conference Proceedings of International Conference on Asia Pacific Psychology, 2009. @conference{yu2009similarity,
title = {Similarity perception of visual objects: A machine-learning approach},
author = {N Y Yu and T Yamauchi and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/yu2009similarity.pdf},
year = {2009},
date = {2009-01-01},
booktitle = {Proceedings of International Conference on Asia Pacific Psychology},
keywords = {Face perception},
pubstate = {published},
tppubtype = {conference}
}
|
2008
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Yamanaka, T; Perera-Lluna, A; Raman, B; Gutierrez-Galvez, A; Gutierrez-Osuna, R Learning Sparse Basis Vectors in Small-Sample Datasets through Regularized Non-Negative Matrix Factorization Technical Report 2008. @techreport{yamanaka08learning,
title = {Learning Sparse Basis Vectors in Small-Sample Datasets through Regularized Non-Negative Matrix Factorization},
author = {T Yamanaka and A Perera-Lluna and B Raman and A Gutierrez-Galvez and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/yamanaka08learning.pdf},
year = {2008},
date = {2008-07-16},
abstract = {This article presents a novel dimensionality-reduction technique, Regularized Non-negative Matrix Factorization (RNMF), which combines the non-negativity constraint of NMF with a regularization term. In contrast with NMF, which degrades to holistic representations with decreasing amount of data, RNMF is able to extract parts of objects even in the small-sample case.},
keywords = {Face perception},
pubstate = {published},
tppubtype = {techreport}
}
This article presents a novel dimensionality-reduction technique, Regularized Non-negative Matrix Factorization (RNMF), which combines the non-negativity constraint of NMF with a regularization term. In contrast with NMF, which degrades to holistic representations with decreasing amount of data, RNMF is able to extract parts of objects even in the small-sample case. |
Rodriguez, J; Bortfeld, H; Gutierrez-Osuna, R Reducing the other-race effect through caricatures Conference IEEE International Conference on Automatic Face & Gesture Recognition, IEEE 2008. @conference{rodriguez2008reducing,
title = {Reducing the other-race effect through caricatures},
author = {J Rodriguez and H Bortfeld and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/rodriguez2008reducing.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {IEEE International Conference on Automatic Face & Gesture Recognition},
pages = {1--5},
organization = {IEEE},
abstract = {We recognize faces from our own race better than those from another race. Although the relative contribution of different mechanisms (e.g. contact vs. attention) remains elusive, it is generally agreed that the other-race effect results from the fact that discriminatory facial features are race-dependent. Previous research has also shown that facial recognition improves when viewers are first familiarized with faces whose most distinctive features have been caricaturized. In this study, we sought to determine the extent to which familiarization with caricaturized faces could also be used to reduce other-race effects. Using an old/new face recognition paradigm, Caucasian subjects were first familiarized with a set of faces from multiple races, and then asked to recognize those faces among a set of confounders. Participants who were familiarized with and then asked to recognize veridical versions of the faces showed a significant other-race effect on Indian faces. In contrast, participants who were familiarized with caricaturized versions of the same faces, and then asked to recognize their veridical versions, showed no other-race effects on Indian faces. This result suggests that caricaturization may be used to help individuals focus their attention to features that are useful for recognition of other-race faces.},
keywords = {Face perception},
pubstate = {published},
tppubtype = {conference}
}
We recognize faces from our own race better than those from another race. Although the relative contribution of different mechanisms (e.g. contact vs. attention) remains elusive, it is generally agreed that the other-race effect results from the fact that discriminatory facial features are race-dependent. Previous research has also shown that facial recognition improves when viewers are first familiarized with faces whose most distinctive features have been caricaturized. In this study, we sought to determine the extent to which familiarization with caricaturized faces could also be used to reduce other-race effects. Using an old/new face recognition paradigm, Caucasian subjects were first familiarized with a set of faces from multiple races, and then asked to recognize those faces among a set of confounders. Participants who were familiarized with and then asked to recognize veridical versions of the faces showed a significant other-race effect on Indian faces. In contrast, participants who were familiarized with caricaturized versions of the same faces, and then asked to recognize their veridical versions, showed no other-race effects on Indian faces. This result suggests that caricaturization may be used to help individuals focus their attention to features that are useful for recognition of other-race faces. |
Yu, N Y; Yamauchi, T; Yang, H F; Chen, Y L; Gutierrez-Osuna, R A Computational Method to Find Salient Features Conference Proceedings of the 6th International Conference of Cognitive Science, 2008. @conference{yu2008Salient,
title = {A Computational Method to Find Salient Features},
author = {N Y Yu and T Yamauchi and H F Yang and Y L Chen and R Gutierrez-Osuna},
url = {https://psi.engr.tamu.edu/wp-content/uploads/2018/01/yu2008Salient.pdf},
year = {2008},
date = {2008-01-01},
booktitle = {Proceedings of the 6th International Conference of Cognitive Science},
abstract = {Although it is well known that people selectively attend to salient features in similarity judgment, no clear method of identifying “salient features” has been proposed. In this study, we present a new computational technique to identify salient features. First, we collected behavioral data from human participants, and this data was simulated with machine learning techniques, which determined optimal allocations of weights of candidate features. Results revealed image-specific sets of salient features for similarity perception, and suggested that people exaggerate differences between features while computing similarity.},
keywords = {Face perception},
pubstate = {published},
tppubtype = {conference}
}
Although it is well known that people selectively attend to salient features in similarity judgment, no clear method of identifying “salient features” has been proposed. In this study, we present a new computational technique to identify salient features. First, we collected behavioral data from human participants, and this data was simulated with machine learning techniques, which determined optimal allocations of weights of candidate features. Results revealed image-specific sets of salient features for similarity perception, and suggested that people exaggerate differences between features while computing similarity. |