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didit-biometric-age-estimation迪迪特生物年龄估计

Agent Skill

didit-biometric-age-estimation 用于处理图像、截图、视觉识别或图片素材相关工作,适合在 OpenClaw 中需要让 Agent 分析图片、整理视觉素材或辅助图像流程时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:didit-biometric-age-estimation(迪迪特生物年龄估计)
来源仓库:https://github.com/rosasalberto/didit-biometric-age-estimation
安装命令:
openclaw skills install didit-biometric-age-estimation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install didit-biometric-age-estimation

简介

通过面部图像估算个体年龄的生物特征识别技术服务。

  • 适用于年龄门禁控制、未成年人保护或身份验证增强场景。
  • 输入图片 URL 或 Base64 编码即可获得置信区间内的年龄预测值。
  • 依赖 Didit 独立 API,准确率受光照、姿态与遮挡因素影响。
  • 禁止用于种族歧视或非法监控目的,须符合当地法律法规要求。

SKILL.md

name
didit-biometric-age-estimation
description
>
version
1.0.0
metadata
openclaw
requires
env
primaryEnv
DIDIT_API_KEY
emoji
🎂
homepage
https://docs.didit.me

Didit Age Estimation API

Overview

Estimates a person's age from a facial image using deep learning. Also performs a passive liveness check to prevent spoofing.

Key constraints:

  • Supported formats: JPEG, PNG, WebP, TIFF
  • Maximum file size: 5MB
  • Image must contain one clearly visible face
  • Accuracy: MAE ±3.5 years overall; ±1.5 years for under-18

Capabilities: Age estimation with confidence scoring, gender estimation, passive liveness detection, configurable age thresholds, per-country age restrictions, adaptive mode with ID verification fallback for borderline cases.

Liveness methods (workflow mode):

MethodSecurityBest For
ACTIVE_3D (Action + Flash)HighestBanking, government, healthcare
FLASHING (3D Flash)HighFinancial services, identity verification
PASSIVE (single-frame CNN)StandardLow-friction consumer apps

API Reference: https://docs.didit.me/standalone-apis/age-estimation Feature Guide: https://docs.didit.me/core-technology/age-estimation/overview


Authentication

All requests require x-api-key header. Get your key from Didit Business Console → API & Webhooks, or via programmatic registration (see below).

Getting Started (No Account Yet?)

If you don't have a Didit API key, create one in 2 API calls:

  1. Register: POST https://apx.didit.me/auth/v2/programmatic/register/ with {"email": "you@gmail.com", "password": "MyStr0ng!Pass"}
  2. Check email for a 6-character OTP code
  3. Verify: POST https://apx.didit.me/auth/v2/programmatic/verify-email/ with {"email": "you@gmail.com", "code": "A3K9F2"} → response includes api_key

To add credits: GET /v3/billing/balance/ to check, POST /v3/billing/top-up/ with {"amount_in_dollars": 50} for a Stripe checkout link.

See the didit-verification-management skill for full platform management (workflows, sessions, users, billing).


Endpoint

POST https://verification.didit.me/v3/age-estimation/

Headers

HeaderValueRequired
x-api-keyYour API keyYes
Content-Typemultipart/form-dataYes

Request Parameters (multipart/form-data)

ParameterTypeRequiredDefaultDescription
user_imagefileYesFacial image (JPEG/PNG/WebP/TIFF, max 5MB)
rotate_imagebooleanNofalseTry 0/90/180/270 rotations for non-upright faces
save_api_requestbooleanNotrueSave in Business Console Manual Checks
vendor_datastringNoYour identifier for session tracking

Example

import requests

response = requests.post(
    "https://verification.didit.me/v3/age-estimation/",
    headers={"x-api-key": "YOUR_API_KEY"},
    files={"user_image": ("selfie.jpg", open("selfie.jpg", "rb"), "image/jpeg")},
    data={"vendor_data": "user-123"},
)
print(response.json())
const formData = new FormData();
formData.append("user_image", selfieFile);

const response = await fetch("https://verification.didit.me/v3/age-estimation/", {
  method: "POST",
  headers: { "x-api-key": "YOUR_API_KEY" },
  body: formData,
});

Response (200 OK)

{
  "request_id": "a1b2c3d4-...",
  "liveness": {
    "status": "Approved",
    "method": "PASSIVE",
    "score": 89.92,
    "age_estimation": 24.3,
    "reference_image": "https://example.com/reference.jpg",
    "video_url": null,
    "warnings": []
  },
  "created_at": "2025-05-01T13:11:07.977806Z"
}

Status Values & Handling

StatusMeaningAction
"Approved"Age verified above threshold, liveness passedProceed with your flow
"Declined"Age below minimum or liveness failedCheck warnings for specifics
"In Review"Borderline case, needs reviewTrigger ID verification fallback or manual review

Error Responses

CodeMeaningAction
400Invalid requestCheck file format, size, parameters
401Invalid API keyVerify x-api-key header
403Insufficient creditsTop up at business.didit.me

Response Field Reference

FieldTypeDescription
statusstring"Approved", "Declined", "In Review", "Not Finished"
methodstring"ACTIVE_3D", "FLASHING", or "PASSIVE"
scorefloat0-100 liveness confidence score
age_estimationfloatEstimated age in years (e.g. 24.3). null if no face
reference_imagestringTemporary URL (expires 60 min)
video_urlstringTemporary URL for active liveness video. null for passive
warningsarray{risk, log_type, short_description, long_description}

Accuracy by Age Range

Age RangeMAE (years)Confidence
Under 181.5High
18-252.8High
26-403.2High
41-603.9Medium-High
60+4.5Medium

Warning Tags

Auto-Decline

TagDescription
NO_FACE_DETECTEDNo face found in image
LIVENESS_FACE_ATTACKSpoofing attempt detected
FACE_IN_BLOCKLISTFace matches a blocklist entry

Configurable (Decline / Review / Approve)

TagDescription
AGE_BELOW_MINIMUMEstimated age below configured minimum
AGE_NOT_DETECTEDUnable to estimate age (image quality, lighting)
LOW_LIVENESS_SCORELiveness score below threshold
POSSIBLE_DUPLICATED_FACESignificant similarity with previously verified face

Warning severity: error (→ Declined), warning (→ In Review), information (no effect).


Common Workflows

Basic Age Gate

1. Capture user selfie
2. POST /v3/age-estimation/ → {"user_image": selfie}
3. Check liveness.age_estimation >= your_minimum_age
4. If "Approved" → user meets age requirement
   If "Declined" → check warnings for AGE_BELOW_MINIMUM or liveness failure

Adaptive Age Estimation (Workflow Mode)

Uses workflow_type: "adaptive_age_verification" — creates a session where borderline ages trigger automatic ID verification fallback.

1. POST /v3/workflows/ → {"workflow_type": "adaptive_age_verification", "is_liveness_enabled": true, "is_age_restrictions_enabled": true}
2. POST /v3/session/ → create session with the workflow_id from step 1
3. User takes selfie → system estimates age
4. Clear pass (well above threshold) → Approved instantly
   Clear fail (well below threshold) → Declined
   Borderline case → automatic ID verification fallback
5. If ID fallback triggered: per-country age restrictions apply

Per-Country Age Restrictions

Configure in Console per issuing country:

CountryMin AgeOverrides
USA18Mississippi: 21, Alabama: 19
KOR19
GBR18
ARE21
Use "Apply age of majority" button in Console to auto-populate defaults.

Utility Scripts

estimate_age.py: Estimate age from a facial image via the command line.

# Requires: pip install requests
export DIDIT_API_KEY="your_api_key"
python scripts/estimate_age.py selfie.jpg
python scripts/estimate_age.py photo.png --threshold 21 --vendor-data user-123

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