CODEWITHSUNDEEP
pythonface-recognition
Mark
Mark

Reputation: 16

How can I reduce false positives in Luxand.cloud face recognition API?

I’m working on an application that uses the Luxand.cloud face recognition API to identify individuals in a crowd. I’m experiencing a high rate of false positives, where the API incorrectly identifies one person as another, particularly in crowded environments.

def recognize_face(image_url):
    endpoint = f'https://api.luxand.cloud/photo/search'
    headers = {
        'token': my_token
    }
    data = {
        'photo': image_url,
        'multiple': 'true' 
    }
    
    response = requests.post(endpoint, headers=headers, data=data)
    
    if response.status_code == 200:
        return response.json()
    else:
        print("Error:", response.status_code, response.text)
        return None

I’ve tried increasing the confidence threshold, but this approach also leads to an increase in false negatives. What techniques can help reduce false positives in Luxand.cloud’s face recognition API? Are there specific API settings or additional checks that can be implemented to improve identification accuracy?

Upvotes: 0

Views: 55

Answers (1)

Cheanastas
Cheanastas

Reputation: 1

Try this improved code, maybe it will help:

import requests

API_KEY = 'your_api_key'
IMAGE_URL = 'https://example.com/your_image.jpg'

def preprocess_image(image_url):
    # Implement image preprocessing here, such as face detection and cropping
    # This is a placeholder; actual implementation depends on your environment
    return image_url

def recognize_face(image_url):
    endpoint = f'https://api.luxand.cloud/photo/search'
    headers = {
        'token': API_KEY
    }
    data = {
        'photo': preprocess_image(image_url),
        'multiple': 'false',  # Set to 'false' to reduce false positives
        # 'age_filter': '20-30', # Optional: Use age filter if applicable
        # 'gender_filter': 'female', # Optional: Use gender filter if applicable
    }
    
    response = requests.post(endpoint, headers=headers, data=data)
    
    if response.status_code == 200:
        results = response.json()
        if results and len(results) > 0:
            best_match = results[0]  # Assuming results are sorted by confidence
            print(f"Detected: {best_match['name']} with confidence {best_match['confidence']}%")
            # Additional logic to handle multiple matches or low confidence
        else:
            print("No faces detected or no confident match.")
    else:
        print("Error:", response.status_code, response.text)
        return None

Upvotes: -1

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