Gender and Age Detection Dataset for Training

Here are some popular datasets you can use for gender and age detection:

1. Adience Dataset

• Description: Contains images for age and gender estimation. The dataset includes real-world face images with varied poses, occlusions, and lighting conditions.
• Age Labels: Grouped into ranges like (0-2), (4-6), (8-13), (15-20), etc.
• Size: ~26,000 face images.
• Link: Adience Dataset

2. IMDB-WIKI Dataset

• Description: The largest publicly available dataset for age and gender prediction. It includes face images labeled with age and gender, sourced from IMDb and Wikipedia.
• Age Labels: Actual age of the individual.
• Size: ~500,000 face images.
• Link: IMDB-WIKI Dataset

3. UTKFace Dataset

• Description: A large-scale dataset with over 20,000 face images labeled for age, gender, and ethnicity. Includes faces of diverse age groups and ethnic backgrounds.
• Age Labels: Actual age of the individual.
• Size: ~20,000 face images.
• Link: UTKFace Dataset

4. FairFace Dataset

• Description: A balanced dataset for race, age, and gender prediction, designed to mitigate biases in facial analysis systems.
• Age Labels: Grouped into ranges like 0-2, 3-9, 10-19, etc.
• Size: ~100,000 images.
• Link: FairFace Dataset

5. AFAD Dataset

• Description: Contains over 165,000 images of Asian faces with age and gender labels, focusing on specific demographics.
• Age Labels: Actual age.
• Size: ~165,000 images.
• Link: AFAD Dataset

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Best Approach for Gender and Age Detection Training

1. Preprocessing Steps

• Face Detection: Use a robust face detection model (e.g., Haar cascades, DLIB, MTCNN, or YOLO) to extract face regions.
• Alignment: Align faces to normalize rotations and scale for consistent inputs.
• Normalization: Resize face images to a fixed size (e.g., 128x128) and normalize pixel values to [0, 1] or [-1, 1].

2. Model Architecture

• CNN-Based Models: Convolutional Neural Networks (CNNs) are well-suited for image-based tasks. Popular architectures include:
  • Lightweight models: MobileNet, EfficientNet (good for edge deployment).
  • Deeper models: ResNet, VGG16, or InceptionNet for higher accuracy.
• Multi-Task Learning (MTL): A shared backbone with separate output layers for gender and age prediction.
  • Example: One softmax layer for gender (Male, Female) and another layer for age regression or classification (age bins).

3. Loss Functions

• Gender Detection:
  • Use Categorical Cross-Entropy for binary classification (Male, Female).
• Age Detection:
  • Regression-based: Use Mean Squared Error (MSE) for predicting exact age.
  • Classification-based: Use Categorical Cross-Entropy for age ranges.

4. Training Process

• Data Augmentation:
  • Random cropping, rotation, horizontal flipping, and brightness adjustments to improve model robustness.
• Transfer Learning:
  • Start with pre-trained models (e.g., ResNet, MobileNet) on ImageNet and fine-tune for gender and age detection tasks.
• Balanced Dataset Handling:
  • If classes (e.g., age or gender) are imbalanced, use techniques like oversampling, weighted loss functions, or SMOTE.

5. Evaluation Metrics

• Gender Detection:
  • Accuracy, Precision, Recall, F1-Score.
• Age Detection:
  • Mean Absolute Error (MAE) for regression tasks.
  • Accuracy for classification tasks.

6. Advanced Approaches

• Attention Mechanisms:
  • Use attention layers to focus on key facial features.
• Ensemble Learning:
  • Combine predictions from multiple models for improved performance.
• Vision Transformers (ViT):
  • Explore transformers for image-based tasks, especially for large datasets.

Tools and Frameworks:

• Deep Learning Frameworks: PyTorch, TensorFlow/Keras.
• Face Detection Libraries: OpenCV, DLIB, MTCNN.

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Example Workflow

1. Prepare Dataset:
   • Download datasets, perform face detection and alignment.
2. Train the Model:
   • Use transfer learning with pre-trained models like ResNet or EfficientNet.
3. Evaluate the Model:
   • Test on unseen data and compute metrics.
4. Deploy the Model:
   • Optimize using ONNX or TensorRT for real-time applications.

By following these steps, you can effectively train a gender and age detection model that performs well even in challenging scenarios.

Adaptive automatic Image enhancing

Adaptive automatic enhancing image for detecting objects. Following cpp opencv code:

 

void AutoEnhanceAdaptivePreprocessing(const cv::Mat& inputImage, cv::Mat& outputImage)
{

// Step 1: Convert to grayscale (if not already)

cv::Mat grayImage;

if (inputImage.channels() == 3)
           cv::cvtColor(inputImage, grayImage, cv::COLOR_BGR2GRAY);

 else
grayImage = inputImage.clone();

//adaptive histogram equalization

// Step 2: Apply CLAHE for adaptive histogram equalization

cv::Ptr<cv::CLAHE> clahe = cv::createCLAHE(2.0, cv::Size(8, 8)); // ClipLimit=2.0, TileGridSize=8x8

cv::Mat claheImage;

clahe->apply(grayImage, claheImage);

// Step 3: Denoising using GaussianBlur

cv::Mat denoisedImage;

cv::GaussianBlur(claheImage, denoisedImage, cv::Size(5, 5), 0);

// Step 4: Enhance contrast and brightness

double alpha = 1.5; // Contrast control (1.0-3.0)

int beta = 20;      // Brightness control (0-100)

cv::Mat contrastEnhancedImage = denoisedImage.clone();

denoisedImage.convertTo(contrastEnhancedImage, -1, alpha, beta);

cv::cvtColor(contrastEnhancedImage, outputImage, cv::COLOR_GRAY2RGB);

}