Face and gait recognition
Two biometrics from video: face recognition by embedding faces into a metric space, and gait recognition from the way a silhouette moves.
Biometrics asks not "what is in this video?" but "who is this?". Two modalities dominate the non-invasive end of the spectrum. Face recognition reads the geometry and texture of a face; it is accurate, needs cooperation, and demands a good view. Gait recognition reads the way a person walks; it works at a distance and in low resolution, but needs several steps and is easy to change.
They are not competitors so much as different operating points, and comparing them clarifies what a biometric actually requires.
Verification, identification, recognition
Keep three tasks apart. Verification asks "is this the claimed person?" and is a one-to-one yes/no at a threshold. Identification asks "who is this?" against a gallery and is one-to-many. Recognition is the informal umbrella. Confusing verification accuracy with closed-set identification accuracy is a recurring source of misleading claims.
Adjust the operating conditions and watch the two modalities cross the same decision threshold.
Tune distance, face resolution and available frames — see which biometric clears the same 0.5 decision threshold.
At these settings the more reliable modality is the gait. — face fails on range/resolution while gait still has enough silhouette signal.
| property | face | gait |
|---|---|---|
| cooperation | required (look at camera) | not required |
| range | metres | tens of metres |
| illumination | needs visible light | works in darkness (IR) |
| resolution | high (face region) | low (whole silhouette) |
| temporal input | single frame | several gait cycles |
| privacy | biometric face data | silhouette, less identifying |
Illustrative score model: exponential range falloff, sigmoid resolution quality and an exposure curve for the number of gait cycles. Real systems report EER/ROC curves on datasets like LFW or CASIA-B; the qualitative trade-offs in the table are the reliable part.
Face recognition as metric learning
Modern face recognition is not template matching; it is embedding. A network maps an aligned face to a vector such that distance encodes identity: photos of the same person are close, photos of different people are far. Training uses a margin-based loss so that, for an anchor , a positive (same identity) and a negative (different),
ArcFace sharpens this by adding an angular margin on a hypersphere and normalising both the embedding and the class weights. At test time, verification compares the cosine similarity of two embeddings to a threshold calibrated from a validation set.
The earlier eigenfaces approach — project a face onto the leading principal components of a face image set and match in that subspace — is the linear ancestor of the same idea, and remains the clearest illustration of why a low-dimensional representation of identity is even possible.
Gait recognition from silhouettes
Gait treats the body as a moving shape. A common representation is the gait energy image (GEI): segment the person over a gait cycle, extract the silhouette each frame, and average them over the cycle. The result is a single image that encodes the characteristic shape and its motion, which a CNN then embeds just as it would a face. Because it uses the whole-body silhouette, gait survives low resolution and distance; because it depends on pose and motion, it degrades with load, footwear, injury, and viewpoint.
Measuring a biometric
A biometric is characterised by the ROC and, more compactly, the equal error rate (EER): the point where false-accept and false-reject rates are equal. Accuracy is quoted only alongside the operating point and the dataset — and for faces, alongside the demographic slice, because error rates are known to vary substantially across populations.
Biometrics raise real ethical stakes
Face recognition is a surveillance technology as much as a classifier. False matches are not abstract; they have led to wrongful arrests. Deployment choices about consent, retention, purpose limitation and demographic fairness are part of the engineering, not optional add-ons, and many jurisdictions now regulate them specifically.
Illustrative vs real
The widget's score curves are illustrative formulas, not measured performance. Real numbers come from benchmarks such as LFW, IJB-C and MegaFace for faces and CASIA-B or OUMVLP for gait. What is genuine here is the decision-threshold framing and the qualitative robustness trade-off: faces need resolution and cooperation, gait needs several cycles and tolerates distance.
Check yourself
Eduspheria wiki · Applied AI, Video analytics
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From the mid-term paper
Modeled on NITJ AI-604, Mid-Term March 2025
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Where next: leaving pixels behind, the next chapter models people as nodes in a network and asks who is central, who influences whom, and how facts connect.