Gender estimation
Gender estimation is the anonymous classification of each visit as male or female based on body shape, gait and clothing — never facial recognition — with results reported only as aggregate counts.
Gender estimation is the anonymous classification of each visit as male or female. The AI looks at body shape, posture, gait and clothing to make the call — it does not analyse or store faces, and it does not identify individuals.
How it’s measured
A visitor is detected as they enter the camera view and tracked through the space as a single anonymous instance. The model assigns an estimated gender to that instance, which is added to the aggregate count for the period. No image or personal record is retained — only the count.
Because the signal comes from full-body cues rather than facial features, gender estimation continues to work in low light, at oblique camera angles, and from overhead views where a face would not be visible.
Output is always aggregate. The dashboard reports the male/female split of footfall, dwell, area entries and conversion, and the breakdown can be combined with Age band to segment by demographic group.
See How accurate is gender recognition? for the 95% accuracy figure and how it is measured.