Facial Verification
Facial Verification with Procrustes analysis
Compare two faces and return a geometric, explainable same-or-different verdict: verification only, with no identification against a database.
Why verification needs geometry
Faces in real captures vary in pose, scale, and translation. Procrustes alignment normalizes those differences geometrically before comparison, so the verdict reflects shape similarity rather than incidental pose.
Core capabilities
Face detection and landmarking
Procrustes geometric alignment
Embedding comparison
Configurable match threshold
Similarity and distance metrics
Verification-only (1:1), no identification
Key benefits
- An explainable geometric basis
- Pose- and scale-invariant comparison
- Threshold-controllable verdicts
- No database matching by design
- Reproducible, parameterized runs
How it works
01
Step 01
Provide two faces
Supply a reference image and a probe image.
02
Step 02
Detect and landmark
Faces are detected and facial landmarks are extracted.
03
Step 03
Align with Procrustes
The probe is aligned to the reference via translation, scale, and rotation.
04
Step 04
Compare and return
Embeddings are compared to produce a match verdict with similarity and distance.
Who it's for
- Identity and access teams
- Onboarding and verification flows
- Researchers validating face-matching methods
Technical characteristics
- Procrustes alignment (closed-form 2D rotation)
- Landmark-to-embedding comparison
- Configurable verification threshold
- Quality metrics on every run
- Pluggable face-detector seam
Frequently asked questions
Coming SoonTwo products,
Two products,
in the works
Multi-Frame Super-Resolution and Facial Verification are in active development. Leave your email and be first to know when they launch.