Search Results for author: Brandon RichardWebster

Found 7 papers, 2 papers with code

Psychophysical-Score: A Behavioral Measure for Assessing the Biological Plausibility of Visual Recognition Models

no code implementations16 Oct 2022 Brandon RichardWebster, Justin Dulay, Anthony DiFalco, Elisabetta Caldesi, Walter J. Scheirer

This article proposes a state-of-the-art procedure that generates a new metric, Psychophysical-Score, which is grounded in visual psychophysics and is capable of reliably estimating perceptual responses across numerous models -- representing a large range in complexity and biological inspiration.

Object Recognition

A Study of the Human Perception of Synthetic Faces

no code implementations8 Nov 2021 Bingyu Shen, Brandon RichardWebster, Alice O'Toole, Kevin Bowyer, Walter J. Scheirer

In this paper, we introduce a study of the human perception of synthetic faces generated using different strategies including a state-of-the-art deep learning-based GAN model.

Face Generation

A Neurobiological Evaluation Metric for Neural Network Model Search

1 code implementation CVPR 2019 Nathaniel Blanchard, Jeffery Kinnison, Brandon RichardWebster, Pouya Bashivan, Walter J. Scheirer

In this paper we introduce a human-model similarity (HMS) metric, which quantifies the similarity of human fMRI and network activation behavior.

Visual Psychophysics for Making Face Recognition Algorithms More Explainable

no code implementations ECCV 2018 Brandon RichardWebster, So Yon Kwon, Christopher Clarizio, Samuel E. Anthony, Walter J. Scheirer

Scientific fields that are interested in faces have developed their own sets of concepts and procedures for understanding how a target model system (be it a person or algorithm) perceives a face under varying conditions.

Face Recognition

PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition

no code implementations19 Nov 2016 Brandon RichardWebster, Samuel E. Anthony, Walter J. Scheirer

By providing substantial amounts of data and standardized evaluation protocols, datasets in computer vision have helped fuel advances across all areas of visual recognition.

To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?

1 code implementation16 Oct 2016 Sandipan Banerjee, Joel Brogan, Janez Krizaj, Aparna Bharati, Brandon RichardWebster, Vitomir Struc, Patrick Flynn, Walter Scheirer

If a CNN is intended to tolerate facial pose, then we face an important question: should this training data be diverse in its pose distribution, or should face images be normalized to a single pose in a pre-processing step?

Face Recognition

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