Face Analyzer by Eachlabs

1019-face-analyzer

Face Analyzer by Each AI is an AI model that detects and analyzes gender, age, and race prediction.

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~16 sec

StatusActive

Version

0.0.1

Updated8 days ago

API Reference

View Full Documentation

Prerequisites

Create an API Key from the Eachlabs Console
Install the required dependencies for your chosen language (e.g., requests for Python)

API Integration Steps

1. Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

import requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "X-API-Key": API_KEY,
    "Content-Type": "application/json"
}

def create_prediction():
    response = requests.post(
        "https://api.eachlabs.ai/v1/prediction/",
        headers=HEADERS,
        json={
            "model": "1019-face-analyzer",
            "version": "0.0.1",
            "input": {
  "image_url": "your face image here"
}
        }
    )
    prediction = response.json()
    
    if prediction["status"] != "success":
        raise Exception(f"Prediction failed: {prediction}")
    
    return prediction["predictionID"]

2. Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

def get_prediction(prediction_id):
    while True:
        result = requests.get(
            f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",
            headers=HEADERS
        ).json()
        
        if result["status"] == "success":
            return result
        elif result["status"] == "error":
            raise Exception(f"Prediction failed: {result}")
        
        time.sleep(1)  # Wait before polling again

3. Complete Example

Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.

try:
    # Create prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = get_prediction(prediction_id)
    print(f"Output URL: {result['output']}")
    print(f"Processing time: {result['metrics']['predict_time']}s")
except Exception as e:
    print(f"Error: {e}")

Additional Information

The API uses a two-step process: create prediction and poll for results
Response time: ~16 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

Eachlabs' Face Analyzer is an advanced deep learning model designed to analyze and process facial images for various purposes, such as age estimation and identity verification. This model leverages state-of-the-art algorithms to deliver accurate and efficient results.

Technical Specifications

Face Verification

Age and Gender Estimation

Facial Feature Analysis (e.g., race and ethnicity)

Key Considerations

Image Quality:

Low-quality images or low resolutions may lead to inaccurate predictions.

If the output is in the format { "age": 25, "gender": "Woman", "race": "white" } and you need to access each value individually in the next steps, select the parameter in the input and write the keyword you want to access after placing a period.

Example:

Tips & Tricks

Make sure there is only one person in the photo.

The person's face should be clearly visible in the photo, with no shadows or half-face visible.

Capabilities

Detect facial expressions with high accuracy.

Predict individuals' age, gender, and ethnicity.

Provide detailed facial analysis for various use cases.

What can I use for?

Authentication and identity verification systems.

Creative applications such as character design or storytelling.

Enhancing image quality for professional or artistic projects

Things to be aware of

Environmental Factors:

The model may struggle in extreme lighting conditions or with occlusions (e.g., sunglasses, masks).

Age Progression:

The model may not reliably predict or verify faces across significant age differences (e.g., comparing a child to an adult).

Non-Facial Variations:

Accessories, hairstyles, or cultural facial features may affect predictions.

Limitations

The model performs best on frontal face images captured under good lighting conditions.

Results may vary with extreme facial angles or occlusions.

Output Format: Text

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