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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
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<meta name="description" content="DESCRIPTION META TAG">
<meta property="og:title" content="How Exposure to Diverse Faces Shapes the Computational Mechanism of Face Perception"/>
<meta property="og:description" content="Poster for CCN 2024"/>
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<title>Inversion Effects in Humans and Deep Neural Networks</title>
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<h1 class="title is-1 publication-title">Inversion Effects in Humans and Deep Neural Networks</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://el4h.github.io" target="_blank">Elaheh Akbarifathkouhi</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://www.katharinadobs.com/" target="_blank">Katharina Dobs</a><sup>1,2</sup>,</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">1 Department of Psychology, Justus Liebig University Giessen
Giessen, 35394, Germany
<br>2 Center for Mind, Brain and Behavior, Universities of Marburg, Giessen, and Darmstadt Marburg, 35032, Germany.
</span>
<span></span><a href="https://www.uni-giessen.de/en/faculties/f06/psy/welcome-to-the-department-of-psychology?set_language=en" target="_blank">Department of Psychology, Justus Liebig University of Giessen</a></span>
<!-- <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span>-->
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<span>Poster</span>
</a>
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</div>
</section>
<!-- Teaser video-->
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</h2>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
The Other-Race Effect (ORE) refers to the difficulty humans experience when recognizing faces from races less familiar to them.
Prior research has linked the ORE with limited exposure to diverse faces, yet the precise nature of this relationship remains unclear.
Here, we use deep convolutional neural networks (CNNs) to investigate how racially varied exposure affects face perception.
We trained three CNNs: one on white faces, another on Asian faces, and a Dual CNN on both. While the single- trained CNNs exhibited an
ORE on the untrained race, the Dual CNN showed less bias and performed well across both races.
Surprisingly, in a target- matching task, the Dual CNN most closely matched both white and Asian participants’
choices, despite their own ORE. Furthermore, only the Dual CNN developed a unified representational space for
both races. When testing on an unfamiliar third race, the Dual CNN outperformed single-trained models,
highlighting its feature space’s generalizability. Our results show that racially diverse exposure not only
reduces biases in CNNs but also results in a unified, more generalized representational geometry,
thereby offering new insights into how experience may shape the computational mechanisms of face perception.
</p>
</div>
</div>
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</div>
</section>
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<h2 class="title is-3">Keywords</h2>
<div class="content has-text-justified">
<p>
Face Perception, Deep Convolutional Neural Networks, Other-Race Effect, Representational Geometry
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
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<h2 class="title is-3">Methods</h2>
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</div>
<h2 class="subtitle has-text-centered">
<b>Methodological Approach:</b> The study's methodology entails using deep neural networks for behavioral response analysis. The Ecoset dataset is selected, containing over 1.5 million images across 565 object categories. The models utilized are AlexNet, VGG-16, and ResNet-50, and the performance of each is measured using the Ecoset test dataset to calculate inversion effects per category.
<br><br>
<b>Participant Selection:</b> 45 participants were chosen, including 28 females and 17 males aged 18-38 years. They engaged in a 10-Way-Classification-Task, with a focus on accuracy and latency of object recognition. Three participants were excluded due to abnormal response patterns and reaction times.
<br><br>
<b>Experimental Stimuli:</b> The experimental stimuli consisted of images from ten categories in the Ecoset test dataset, chosen to represent varying inversion effects. These categories ranged from objects like cogwheels to dolphins. The ten categories were chosen for their consistent inversion scores across all three models.
<br><br>
<b>Experimental Paradigm:</b> A 10-Way-Classification-Task was conducted to study inversion effects in humans and deep neural networks. Each participant/network was shown 100 images in both upright and inverted orientations and had to identify the category of each image. This was done under specific conditions, including a short presentation time of 100 ms, to ensure comparable feedforward processing to neural networks.
<br><br>
</h2>
</div>
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</section>
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<!-- Youtube video -->
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<h2 class="title is-3">Results</h2>
<br>
<h3 class="title is-4">Deep Neural Networks</h3>
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<div class="item">
<!-- Your image here -->
<img src="static/images/deep1.PNG" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
The research found that inversion significantly decreases recognition accuracy in all models (ResNet, AlexNet, and VGG-16), but the model architecture has no significant influence on the extent of the inversion effect. This result, consistent with previous studies, suggests that perceptual phenomena like the Thatcher effect or mirror confusion in deep neural networks may be due to previous experience with faces or objects, not the specific network architecture.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/deep2.PNG" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
The degree of orientation significantly influences object recognition in deep neural networks, with all models (AlexNet, ResNet, VGG-16) showing best recognition performance at upright orientation and additional peaks at 90 and 180 degrees. These findings emphasize the importance of understanding preprocessing steps during training, such as data augmentation, which may have a greater impact on results than the choice of model architecture.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/deep3.PNG" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Inversion Effects from the selected object categories differ significantly. Stimuli from these categories were chosen for the behavioral study with finetuned deep nets and humans.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/deep4.PNG" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
A representational similarity analysis (RSA) was conducted on ResNet-50, AlexNet, and VGG-16 models, revealing no significant differences in activations in the penultimate layer of the neural networks based on the orientation of stimuli. Despite expectations, inverted and upright images are represented similarly in the networks' features, with late layers displaying more invariance to image transformations like rotation or translations.
</h2>
</div>
</div>
<br><br><br>
<h3 class="title is-4">Humans</h3>
<div id="results-carousel" class="carousel results-carousel">
<div class="item">
<!-- Your image here -->
<img src="static/images/human1.PNG" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
A paired-samples t-test revealed a significant decrease in recognition performance for inverted images compared to upright ones.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/human2.PNG" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
A two-way repeated-measures ANOVA on recognition accuracy across object category and orientation revealed significant main effects for orientation and category, but not for their interaction, indicating no significant difference in inversion effects across groups. However, post-hoc analysis showed a significant decrease in recognition performance for inverted images in the 'weasel' category, decreasing recognition performance by about 5 percent. No significant differences in inversion effects were found across the other nine categories, and this may be attributed to efficient face recognition mechanisms being recruited during the recognition of animate objects and to the nature of the experimental design and the selected categories. It's suggested that object categories are processed holistically when exemplar individuation is needed, the category belongs to a homogeneous group, and the objects have a canonical orientation.
</h2>
</div>
</div>
<br><br><br>
<h3 class="title is-4">Humans vs. Deep Neural Networks</h3>
<div id="results-carousel" class="carousel results-carousel">
<div class="item">
<!-- Your image here -->
<img src="static/images/humandeep1.PNG" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
To investigate whether humans and neural networks exhibit similar behaviors, neural networks were modified to match a 10-way classification task, trained with the Ecoset pre-trained models, and tested on upright and inverted images. Misclassifications
in humans and neural networks mainly pointed to problems with classes that are
conceptually similar. In my data, eggplants were misclassified as onions and vice versa.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/humandeep2.PNG" alt="MY ALT TEXT"/>
<h2 class="subtitle has-text-centered">
Results showed significantly high correlations in response patterns between humans and models, suggesting similarities in classification behaviors. When analyzing error distributions by excluding correct responses, correlations between humans and neural networks were found to be low to moderate, with ResNet and VGG-16 showing the highest similarity to human errors. However, divergent classification patterns between humans and deep neural networks (DNNs) emerged when the level of image distortion increased, with misclassifications often occurring among conceptually similar classes, suggesting that current neural network architectures may need further refinement to better emulate human behavior.
</h2>
</div>
</div>
</div>
</section>
<!-- End youtube video -->
<!-- Video carousel -->
<section class="hero is-small">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<br><br><br>
<h2 class="title is-3">Main Finding</h2>
<div class="content has-text-justified">
<p>
This thesis investigated inversion effects (recognition accuracy for images presented upside down versus right side up) in both humans and deep neural networks (DNNs). The findings suggest that DNNs do exhibit significant inversion effects, which are also reflected across different object categories. However, the architecture of the neural network does not impact the magnitude of this effect. In humans, no significant differences were found across object categories, but the response patterns between humans and DNNs showed strong similarities. Future research could focus on identifying which object properties amplify these inversion effects in neural networks, and then confirm these properties in humans. This could help further understand the origins of the face inversion effect, which is the greater difficulty people have in recognizing faces presented upside down compared to other objects.
</p>
<br>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title">Poster</h2>
<iframe src="static/pdfs/Poster_SamuelSander.pdf" width="100%" height="1300">
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</section>
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<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">References</h2>
<pre><code>
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