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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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The Face Inversion Effect in Deep Convolutional Neural Networks.

Fang Tian1, Hailun Xie2, Yiying Song2

  • 1State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.

Frontiers in Computational Neuroscience
|May 26, 2022
PubMed
Summary

The face inversion effect shows inverted faces are harder to recognize than objects. This study used an artificial face system to find that inverted faces are processed like objects, supporting a key hypothesis in face perception.

Keywords:
AlexNetVGG-Facedeep convolutional neural networkface inversion effectface system

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Area of Science:

  • Cognitive Neuroscience
  • Computer Vision
  • Artificial Intelligence

Background:

  • The face inversion effect (FIE) is a key indicator of specialized face processing.
  • It's hypothesized that upright faces use face-specific mechanisms, while inverted faces are treated as objects.
  • Neuroimaging studies have yielded inconclusive results regarding this hypothesis.

Purpose of the Study:

  • To investigate the FIE in an artificial face system, the VGG-Face deep convolutional neural network (DCNN).
  • To test the hypothesis that inverted faces are processed as objects within a dedicated face system.
  • To clarify the neural mechanisms underlying face perception and the FIE.

Main Methods:

  • Examined the FIE in VGG-Face, a DCNN trained for face identification.
  • Compared VGG-Face's FIE to a DCNN pretrained for general object processing.
  • Conducted classification error analysis and examined internal representations within VGG-Face.

Main Results:

  • VGG-Face exhibited a stronger FIE compared to the object-processing DCNN.
  • Inverted faces were miscategorized as objects by VGG-Face.
  • Internal representations showed that VGG-Face processed inverted faces similarly to objects.

Conclusions:

  • The study supports the hypothesis that inverted faces are represented as objects within a pure face system.
  • Artificial neural networks can serve as valuable models for understanding human cognitive processes like face perception.
  • Findings provide insights into the distinct processing of upright versus inverted faces.