# Cinderella's shoe won't fit Soundarya: An audit of facial processing tools on Indian faces

arXiv: https://arxiv.org/abs/2112.09326 · HTML: https://mlsanity.com/paper/2112.09326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 81 references that are in the map.
- First posted on arXiv 17 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019)
- [Assessing Gender Bias in Machine Translation -- A Case Study with Google Translate](https://mlsanity.com/paper/1809.02208/) (2018)
- [Re-imagining Algorithmic Fairness in India and Beyond](https://mlsanity.com/paper/2101.09995/) (2021)
- [Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing](https://mlsanity.com/paper/2001.00964/) (2020)
- [Demoting Racial Bias in Hate Speech Detection](https://mlsanity.com/paper/2005.12246/) (2020)
- [One Label, One Billion Faces: Usage and Consistency of Racial Categories in Computer Vision](https://mlsanity.com/paper/2102.02320/) (2021)
- [The Indian Spontaneous Expression Database for Emotion Recognition](https://mlsanity.com/paper/1512.00932/) (2015)
- [IIITM Face: A Database for Facial Attribute Detection in Constrained and Simulated Unconstrained Environments](https://mlsanity.com/paper/1910.01219/) (2019)

## Built on by (most cited first, 1 of 1)

- [Mask-up: Investigating Biases in Face Re-identification for Masked Faces](https://mlsanity.com/paper/2402.13771/) (2024)

## Abstract (arXiv)

The increasing adoption of facial processing systems in India is fraught with concerns of privacy, transparency, accountability, and missing procedural safeguards. At the same time, we also know very little about how these technologies perform on the diverse features, characteristics, and skin tones of India's 1.34 billion-plus population. In this paper, we test the face detection and facial analysis functions of four commercial facial processing tools on a dataset of Indian faces. The tools display varying error rates in the face detection and gender and age classification functions. The gender classification error rate for Indian female faces is consistently higher compared to that of males -- the highest female error rate being 14.68%. In some cases, this error rate is much higher than that shown by previous studies for females of other nationalities. Age classification errors are also high. Despite taking into account an acceptable error margin of plus or minus 10 years from a person's actual age, age prediction failures are in the range of 14.3% to 42.2%. These findings point to the limited accuracy of facial processing tools, particularly for certain demographic groups, and the need for more critical thinking before adopting such systems.