  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.
