Using Azure AI Vision to perform image analysis and gender prediction

This article is an example of a C# .NET program that uses Azure AI Vision (formerly Azure Cognitive Services) to perform image analysis and attempt to recognize whether a human in an image is male or female based on facial attributes. Azure AI Vision provides pre-trained models for face detection and attribute analysis, including gender prediction. This approach is practical for rapid development, as it leverages a cloud-based API rather than requiring you to train a custom model from scratch.

Prerequisites

• Azure Subscription: You need an Azure account with a Computer Vision or AI Vision resource. You can create a free account with $200 credit at azure.microsoft.com.
• NuGet Packages: Install the Azure.AI.Vision.ImageAnalysis package.
• API Key and Endpoint: Obtain these from your Azure portal after creating a Computer Vision resource.

Steps

1. Set up your Azure Computer Vision resource.
2. Install the required NuGet package in your .NET project.
3. Write the code to analyze an image and extract gender attributes.

Example Code

using Azure;
using Azure.AI.Vision.ImageAnalysis;
using System;
using System.IO;

class Program
{
    static void Main(string[] args)
    {
        // Replace with your Azure Computer Vision endpoint and key
        string endpoint = "https://<your-resource-name>.cognitiveservices.azure.com/";
        string key = "<your-api-key>";
        string imagePath = "<path-to-your-image>.jpg"; // Local image file path

        try
        {
            // Create an Image Analysis client
            ImageAnalysisClient client = new ImageAnalysisClient(
                new Uri(endpoint),
                new AzureKeyCredential(key));

            // Read the image file
            byte[] imageData = File.ReadAllBytes(imagePath);

            // Analyze the image for faces and their attributes
            ImageAnalysisResult result = client.Analyze(
                BinaryData.FromBytes(imageData),
                VisualFeatures.Faces);

            // Process the results
            if (result.Faces != null && result.Faces.Count > 0)
            {
                foreach (var face in result.Faces)
                {
                    Console.WriteLine($"Face detected:");
                    Console.WriteLine($"  Gender: {face.Gender}");
                    Console.WriteLine($"  Age: {face.Age}");
                    Console.WriteLine($"  Bounding Box: {face.FaceRectangle}");
                }
            }
            else
            {
                Console.WriteLine("No faces detected in the image.");
            }
        }
        catch (Exception ex)
        {
            Console.WriteLine($"Error: {ex.Message}");
        }
    }
}

How It Works

1. Setup: The program initializes an ImageAnalysisClient using your Azure endpoint and API key.
2. Image Input: It reads a local image file (e.g., .jpg) into a byte array. Alternatively, you can modify the code to use a URL by passing new Uri(imageUrl) instead of BinaryData.FromBytes.
3. Analysis: The Analyze method is called with the VisualFeatures.Faces option, which instructs Azure to detect faces and extract attributes like gender and age.
4. Output: The program iterates through detected faces and prints the gender (e.g., “Male” or “Female”), age, and bounding box coordinates.

Installation

1. Create a new .NET console app:

   dotnet new console -n ImageGenderRecognition
   cd ImageGenderRecognition
   

2. Add the Azure AI Vision package:

   dotnet add package Azure.AI.Vision.ImageAnalysis
   

3. Replace <your-resource-name>, <your-api-key>, and <path-to-your-image> in the code with your actual Azure endpoint, key, and image path.
4. Run the program:

   dotnet run
   

Notes

• Azure AI Vision: The service uses pre-trained models to predict gender based on facial features. Gender is reported as “Male” or “Female” (or sometimes “Unknown” if confidence is low). Be aware that gender prediction from images can be imprecise and should be used cautiously, especially in sensitive applications, due to ethical considerations and potential biases in training data.
• Limitations: The model may not always accurately predict gender, especially with diverse or ambiguous facial features. For higher accuracy, you’d need a custom-trained model, which requires significant data and effort.
• Alternative Approach: If you prefer a custom model, you can use ML.NET with a pre-trained TensorFlow model (e.g., ResNet50) and fine-tune it on a labeled dataset of male/female images. This is more complex and requires a dataset, so I chose the Azure approach for simplicity.
• Ethical Considerations: Gender recognition from images can raise privacy and bias concerns. Ensure your use case complies with ethical guidelines and regulations (e.g., GDPR, CCPA).
• Source: This example is adapted from Microsoft’s documentation on Azure AI Vision.

Sample Output

For an image with a detected face:

Face detected:
  Gender: Male
  Age: 35
  Bounding Box: {X=120,Y=150,Width=200,Height=250}

If no faces are found:

No faces detected in the image.

Improvements

• Test with URLs: Modify the code to accept image URLs instead of local files.
• Add Error Handling: Enhance the code to handle network issues or invalid images.
• Explore Other Features: Azure AI Vision can also detect emotions, facial landmarks, or objects. Check the documentation for more options.
• Custom Model: If you need higher accuracy, explore ML.NET tutorials for training a custom image classification model.