The Face Image Meta-Database (fIMDb) & ChatLab Facial Anomaly Database (CFAD): Tools for research on face perception
Clifford I Workman1,2,3, Anjan Chatterjee1,2,3
1Department of Neurology, University of Pennsylvania, Philadelphia, PA.
Methods in Psychology (Online)
|August 9, 2021
Summary
Researchers can now access the Face Image Meta-Database (fIMDb) and ChatLab Facial Anomaly Database (CFAD) for diverse and representative face image datasets. These resources improve research quality and inclusivity in face recognition and perception studies.
Area of Science:
- Psychology
- Computer Science
- Biomedical Imaging
Background:
- High-quality face image datasets are crucial for scholarly research, including experimental stimuli and algorithm benchmarking.
- Existing face databases often lack diversity in race, ethnicity, and age, leading to unrepresentative scientific findings.
- There is a significant need for databases featuring faces with visible differences to study perception and attitudes towards anomalies.
Purpose of the Study:
- To introduce the Face Image Meta-Database (fIMDb) for efficient discovery of suitable face image datasets.
- To present the ChatLab Facial Anomaly Database (CFAD) to address the scarcity of datasets with diverse facial anomalies.
- To enhance representativeness and quality in face-related research.
Main Methods:
- The fIMDb was developed as a searchable index of existing face databases with filtering capabilities for demographic diversity.
- The CFAD was constructed by collecting photographs of faces exhibiting various visible differences, covering diverse ethnicities and age groups.
- Both databases are made accessible through a central online resource.
Main Results:
- The fIMDb empowers researchers to identify face image databases that meet specific criteria for race, ethnicity, and age.
- The CFAD provides a unique, well-characterized collection of faces with visible differences, suitable for studying perception and attitudes.
- Both resources are available at https://cliffordworkman.com/resources/.
Conclusions:
- The fIMDb and CFAD significantly improve access to diverse and representative face image datasets for scientific investigation.
- These resources are expected to foster more inclusive and robust research in face recognition, perception, and related fields.
- Researchers are encouraged to utilize these databases to advance the quality and generalizability of their findings.
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