cnn-age-v0

Kámárí CNN Age Model (v0)

A small, calibrated CNN that produces the age-gating signal for Kámárí, an African-focused, privacy-first age verification system. It estimates age, the probability of being under 18, and an uncertainty, from a single face crop. It is a signal, not a standalone gate.

• Backbone: EfficientNetV2-S (tf_efficientnetv2_s), ImageNet-pretrained.
• Heads: age regression, under-18 logit, heteroscedastic (aleatoric) uncertainty.
• Trained 30 epochs on an H200 (batch 512, AdamW lr 3e-4 wd 1e-4, cosine schedule, bf16; ~15 min wall-clock; 22,224 train / 2,529 val exact-age rows). Selection minimizes MAE + 5 x MPTR@18 (a child-safety composite). The full run is tracked in Weights & Biases (project kamari).

Intended use

Input: one detected, cropped, 224x224 RGB face. Output: {estimated_age, p_under_18, uncertainty, face_quality}. A downstream policy engine (conservative through the 18 to 21 band), liveness, and a guardian flow turn the signal into a decision. Not for legal age determination and not for 1:N face search.

Results (held-out benchmark, n=8,322)

MAE by skin band: very_light 5.46, light 5.72, intermediate 5.50, tan 5.99, brown 6.23, dark 6.58. MAE by age band: 0-12 4.04, 13-15 6.10, 16-17 5.37, 18-20 4.85, 21-25 4.30, 26-35 5.22, 36-50 6.67, 51+ 8.51. GPU eval latency p50 14.2 ms.

Training curves

Pulled from the Weights & Biases run (project kamari, run cnn-tf_efficientnetv2_s). Training loss is a heteroscedastic Gaussian NLL, so it is allowed to go negative as the variance head sharpens; validation MAE falls to 5.74 and the safety metric (MPTR@18) is tracked alongside it.

Limitations and safety

MAE is competitive, but MPTR@18 is high: about a third of true minors are scored as adults, and higher for dark and brown skin. So the CNN must not gate alone. Kámárí mitigates this with a conservative policy (challenge band up to 21), uncertainty routing, on-device liveness, and a guardian consent flow. Lowering MPTR needs more 13 to 17 and African-labelled training data. Minor-Pass-Through Rate (MPTR) is the metric to track, not MAE.

Training data

Open, license-checked face datasets (UTKFace, APPA-REAL, AgeDB, FG-NET for exact age; FAGE_v2 and FairFace for African signal) with an auto label-quality gate, MTCNN face crops, ITA skin-tone banding, and a leakage-free split. Composite sampling boosts ages 13 to 21 (3x) and dark/brown skin (1.5x). Full methodology: https://github.com/Mystique1337/kamari/blob/main/docs/methodology.md

Files

best.pt (PyTorch weights), cnn_v0.onnx, thresholds_v0.json, metrics_v0.json, and reports. Serving loads best.pt on CPU with OpenCV face detection and crop (matching the training crops).

License

Apache-2.0. This is an estimate, not a legal age determination.

Links

• Code (Apache-2.0): https://github.com/Mystique1337/kamari
• Live demo: https://kamari.shinzii.tech
• Methodology: https://github.com/Mystique1337/kamari/blob/main/docs/methodology.md