Summary of Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration, by Praveen Kumar Chandaliya et al.
Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration
by Praveen Kumar Chandaliya, Kiran Raja, Raghavendra Ramachandra, Zahid Akhtar, Christoph Busch
First submitted to arxiv on: 2 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach is proposed to address biases in Face Recognition Systems (FRS), particularly those exhibited by commercial systems. The method involves generating synthetic face images with altered ethnicity and skin tones to increase the diversity of datasets. A balanced dataset representing three ethnicities (Asian, Black, and Indian) is constructed, and existing Generative Adversarial Network-based models are used for ethnicity alteration. The suitability of these datasets for FRS is assessed using Individual Typology Angle and Face image quality assessment approaches. Four different systems are analyzed to evaluate their performance. This work paves the way for future research on developing specific ethnicity alteration models, expanding datasets with diverse skin tones, and creating databases representing various ethnicities to mitigate bias while addressing privacy concerns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make face recognition systems more fair by using fake images that change a person’s ethnicity or skin tone. The researchers created a special dataset with many pictures of people from different ethnic backgrounds (Asian, Black, and Indian). They then used computer programs to change the ethnicity or skin tone of these images. This helps to fix biases in current face recognition systems that can unfairly affect certain groups of people. The study also tested how well this new approach works by using four different face recognition systems. |
Keywords
» Artificial intelligence » Face recognition » Generative adversarial network
