Identification of faces obscured by noise: comparison of an artificial neural network with human observers

Blackwell, Kim T.; Vogl, Thomas P.; Dettmar, Hans P.; Brown, Michael A.; Barbour, Garth S.; Alkon, Daniel L.

doi:10.1080/095281397147004

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…One influential idea is the observation that the first principal component of (unnormalized) faces is informative about gender (Abdi et al, 1995;O'Toole, Abdi, Deffenbacher, & Valentin, 1993;O'Toole et al, 1998;Sirovich & Kirby, 1987;Turk & Pentland, 1991;Valentin, Abdi, Edelman, & O'Toole, 1997). To evaluate the algorithms described above and their plausibility as models of human face processing, their overall performance (in percentage correct) is compared against the performance of human observers (Blackwell et al, 1997;Hancock, Bruce, & Burton, 1998). We extend these studies by trying to build algorithms which not only predict the over-all percentage correct of human observers, but also their responses on a stimulus by stimulus level (Graf, Wichmann, Bulthoff, & Scholkopf, 2006).…”

Section: Introductionmentioning

confidence: 99%

Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces

Macke

Wichmann

2010

Journal of Vision

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One major challenge in the sensory sciences is to identify the stimulus features on which sensory systems base their computations, and which are predictive of a behavioral decision: they are a prerequisite for computational models of perception. We describe a technique (decision images) for extracting predictive stimulus features using logistic regression. A decision image not only defines a region of interest within a stimulus but is a quantitative template which defines a direction in stimulus space. Decision images thus enable the development of predictive models, as well as the generation of optimized stimuli for subsequent psychophysical investigations. Here we describe our method and apply it to data from a human face classification experiment. We show that decision images are able to predict human responses not only in terms of overall percent correct but also in terms of the probabilities with which individual faces are (mis-) classified by individual observers. We show that the most predictive dimension for gender categorization is neither aligned with the axis defined by the two class-means, nor with the first principal component of all faces-two hypotheses frequently entertained in the literature. Our method can be applied to a wide range of binary classification tasks in vision or other psychophysical contexts.

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Section: Introductionmentioning

confidence: 99%

Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces

Macke

Wichmann

2010

Journal of Vision

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“…It has been used extensively in recent years to solve different problems related to pattern recognition. Examples of areas where the PNN was used successfully are email security enhancement [1], intrusion detection within computer networks [2], water quality assessment [3], health and disease diagnosis [4,5], detection of resistivity for antibiotics [6], biometrics applications [7][8][9], and many military applications [10,11].…”

Section: Introductionmentioning

confidence: 99%

A reduced probabilistic neural network for the classification of large databases

Lotfi¹,

Benyettou²

2014

Turk J Elec Eng & Comp Sci

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Abstract:The probabilistic neural network (PNN) is a special type of radial basis neural network used mainly for classification problems. Due to the size of the network after training, this type of network is usually used for problems with a small-sized training dataset. In this paper, a new training algorithm is presented for use with large training databases. Application to the handwritten digit database shows that the reduced PNN performs better than the standard PNN for all of the studied cases with a big gain in size and processing speed. This new type of neural network can be used easily for problems with large training databases like biometrics and data mining applications. An extension of the network is possible for new training samples and/or classes without retraining.

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Image Analysis and Computer Vision: 1995

Rosenfeld

1996

Computer Vision and Image Understanding

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Identification of faces obscured by noise: comparison of an artificial neural network with human observers

Cited by 6 publications

References 32 publications

Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces

Estimating predictive stimulus features from psychophysical data: The decision image technique applied to human faces

A reduced probabilistic neural network for the classification of large databases

Image Analysis and Computer Vision: 1995

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