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name: face-compare description: Compare two face images and return similarity score using iFlytek Face Recognition API. homepage: https://www.xfyun.cn/doc/face/xf-silent-in-vivo-detection/API.html metadata: { "openclaw": { "emoji": "👤", "requires": { "bins": ["python3"], "env": ["XF_FACE_APP_ID", "XF_FACE_API_KEY", "XF_FACE_API_SECRET"] }, "primaryEnv": "XF_FACE_APP_ID" } }

👤 Face Compare

Compare two face images and calculate their similarity score using iFlytek's advanced face recognition technology.

Designed for identity verification, face matching, and security authentication scenarios.

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✨ Features

• High-accuracy face comparison
• Base64 image encoding support
• Multiple image format support (jpg, png, bmp)
• Detailed similarity scoring
• One-command execution

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🚀 Usage

python {baseDir}/scripts/index.py "<image1_path>" "<image2_path>"

Example:

python {baseDir}/scripts/index.py "/path/to/face1.jpg" "/path/to/face2.jpg"

📋 Input Specification

Image Requirements

• Supported formats: JPG, PNG, BMP
• File size: < 4MB recommended
• Image should contain clear, frontal face
• One face per image for best results

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⚠ Constraints

• Both image paths must be valid and accessible
• Images must contain detectable faces
• API credentials must be configured
• Network connection required

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🔧 Environment Setup

Required:

• Python available in PATH
• Environment variables configured:

export XF_FACE_APP_ID=your_app_id
export XF_FACE_API_KEY=your_api_key
export XF_FACE_API_SECRET=your_api_secret

Or configure it in ~/.openclaw/openclaw.json:

{
	"env": {
		"XF_FACE_APP_ID": "your_app_id",
		"XF_FACE_API_KEY": "your_api_key",
		"XF_FACE_API_SECRET": "your_api_secret"
	}
}

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📦 Output

Returns JSON response with:

• Similarity score (0-100)
• Comparison result (same person or not)
• Confidence level
• Face detection status

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🎯 Target Use Cases

• Identity verification
• Access control systems
• Duplicate account detection
• Photo matching services
• Security authentication
• Attendance systems

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🛠 Extensibility

Future enhancements may include:

• Batch face comparison
• Face quality assessment
• Multiple face detection
• Liveness detection integration
• Custom threshold configuration

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Built for automation workflows and AI-driven identity verification.