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Perceptual and Category Processing of the Uncanny Valley Hypothesis' Dimension of Human Likeness: Some Methodological Issues

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Demographic Estimation from Face Images: Human vs. Machine Performance.

Hu Han, Charles Otto, Xiaoming Liu

IEEE Transactions on Pattern Analysis and Machine Intelligence

|September 11, 2015

Summary

This summary is machine-generated.

This study introduces a new framework for automatic demographic estimation (age, gender, race) from face images. The approach achieves superior performance, offering insights into human perception of facial demographics.

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Area of Science:

Computer Vision
Biometrics
Machine Learning

Background:

Automatic demographic estimation (age, gender, race) from face images has numerous applications.
Accurate age estimation is challenging due to significant variations in facial appearance within demographic groups.

Purpose of the Study:

To present a generic framework for automatic demographic estimation (age, gender, race) from face images.
To develop a quality assessment method for identifying low-quality images unsuitable for reliable demographic estimation.

Main Methods:

Feature extraction using a boosting algorithm.
A hierarchical approach involving between-group classification and within-group regression.
Quality assessment for low-quality face image identification.

Main Results:

The proposed framework demonstrated superior performance on diverse datasets (FG-NET, FERET, MORPH II, PCSO, LFW) compared to state-of-the-art methods.
Crowdsourcing experiments provided insights into human demographic perception from faces.

Conclusions:

The developed framework offers an effective solution for automatic demographic estimation.
Comparing algorithmic and human perception highlights the complexities of facial demographic analysis.