FaceVerifyAI-Advanced

A high-performance, multi-task convolutional neural network (CNN) engineered for real-time facial attribute analysis, specializing in precise age estimation, gender classification, and emotion recognition from facial images.

📋 Overview

FaceVerifyAI-Advanced is a robust computer vision model developed by QuantaSparkLabs. Released in 2026, this model is built on a custom CNN architecture, delivering highly accurate multi-attribute facial analysis. It is optimized for efficiency and reliability, making it suitable for deployment in security systems, user experience personalization, and interactive applications.

The model is trained end-to-end on a custom synthetic dataset, featuring shared convolutional layers for feature extraction and dedicated task-specific heads for attribute prediction.

✨ Core Features

🎯 Multi-Task Analysis	⚡ Technical Excellence
Age Prediction: Continuous age estimation with high precision.	Optimized Architecture: Custom CNN with shared backbone and task-specific heads.
Gender Classification: Binary gender classification with exceptional accuracy.	Efficient Training: Trained with advanced regularization to prevent overfitting.
Emotion Recognition: Classifies fundamental emotional states from facial features.	Production Ready: Designed for real-time inference with a stable, lightweight footprint.

📊 Performance Benchmarks

🏆 Final Validation Metrics

After 50 training epochs, the model achieved exceptional results on the validation set:

Gender Accuracy: 100.00%
Emotion Accuracy: 100.00%
Age MAE (Mean Absolute Error): 0.0990 years

🔬 Reliability & Robustness

The model was trained and validated on a structured, custom synthetic dataset, demonstrating strong generalization on the held-out validation set. Its multi-task design ensures correlated facial features benefit all prediction tasks simultaneously.

🏗️ Model Architecture

High-Level Pipeline

The architecture follows a streamlined, multi-head design:

Input Image (3xHxW)
        ↓
[Shared CNN Backbone]
        ↓
[Shared Fully Connected Layers]
        ↓
    ┌──────┼──────┐
    ↓      ↓      ↓
 Age Head Gender Head Emotion Head
 (Regression) (Classifier) (Classifier)

Technical Design

Backbone: A custom 3-layer CNN with Batch Normalization and Max-Pooling for robust spatial feature extraction.
Feature Integration: Extracted features are flattened and processed through shared dense layers (512 → 256 units).
Task Heads: Separate linear output layers for age (1 neuron, regression), gender (2 neurons), and emotion (3 neurons) tasks.

🔧 Technical Specifications

Parameter	Value
Input Format	RGB Image Tensor (Channels x Height x Width)
Backbone	Custom 3-Layer Convolutional Neural Network (CNN)
Training Epochs	50
Optimizer	Adam (`lr`=0.0001, `betas`=(0.9, 0.999))
Learning Rate Scheduler	ReduceLROnPlateau (factor=0.5, patience=5)
Loss Functions	Age: Mean Squared Error (MSE) Gender & Emotion: Cross-Entropy Loss
Regularization	Dropout (p=0.3), Batch Normalization

Dataset Composition

Type: Custom Structured Synthetic Face Dataset
Total Samples: 10,000
Training Split: 8,000 samples
Validation Split: 2,000 samples
Attributes: Age (continuous), Gender (binary), Emotion (3 classes)

💻 Quick Start

Installation

Ensure you have PyTorch installed. The model requires only core libraries.

pip install torch torchvision

Basic Usage: Loading and Inference

This example shows how to load the model and make a prediction on a preprocessed image tensor.

import torch
import torch.nn as nn
import torch.nn.functional as F

# 1. Define the model architecture (must match training)
class AdvancedFaceVerifyAI(nn.Module):
    def __init__(self, num_gender_classes=2, num_emotion_classes=3):
        super(AdvancedFaceVerifyAI, self).__init__()
        # Convolutional backbone
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.dropout = nn.Dropout(0.3)
        
        # Calculate flattened dimension
        self._to_linear = 128 * (64 // (2**3)) * (64 // (2**3))
        
        # Shared fully connected layers
        self.fc1 = nn.Linear(self._to_linear, 512)
        self.fc_bn1 = nn.BatchNorm1d(512)
        self.fc2 = nn.Linear(512, 256)
        self.fc_bn2 = nn.BatchNorm1d(256)
        
        # Task-specific output heads
        self.age_head = nn.Linear(256, 1)            # Regression
        self.gender_head = nn.Linear(256, num_gender_classes) # Classification
        self.emotion_head = nn.Linear(256, num_emotion_classes) # Classification

    def forward(self, x):
        # Forward pass through the network
        x = self.pool(F.relu(self.bn1(self.conv1(x))))
        x = self.dropout(x)
        x = self.pool(F.relu(self.bn2(self.conv2(x))))
        x = self.dropout(x)
        x = self.pool(F.relu(self.bn3(self.conv3(x))))
        x = self.dropout(x)
        
        x = x.view(-1, self._to_linear)
        
        x = F.relu(self.fc_bn1(self.fc1(x)))
        x = self.dropout(x)
        x = F.relu(self.fc_bn2(self.fc2(x)))
        x = self.dropout(x)
        
        age_out = self.age_head(x)
        gender_out = self.gender_head(x)
        emotion_out = self.emotion_head(x)
        
        return age_out, gender_out, emotion_out

# 2. Instantiate and load the pre-trained weights
model = AdvancedFaceVerifyAI(num_gender_classes=2, num_emotion_classes=3)

# Download 'best_advanced_face_verify_ai_model.pth' from the model repo first
state_dict = torch.load('best_advanced_face_verify_ai_model.pth', map_location='cpu')
model.load_state_dict(state_dict)
model.eval()  # Set to evaluation mode

print("✅ FaceVerifyAI-Advanced model loaded successfully!")

# 3. Run inference (assuming 'image_tensor' is your preprocessed input)
# with torch.no_grad():
#     age_pred, gender_pred, emotion_pred = model(image_tensor)
#     
#     predicted_age = age_pred.item()
#     predicted_gender = torch.argmax(gender_pred, dim=1).item()  # Returns 0 or 1
#     predicted_emotion = torch.argmax(emotion_pred, dim=1).item() # Returns 0, 1, or 2
#     
#     print(f"Predicted Age: {predicted_age:.2f} years")
#     print(f"Predicted Gender Index: {predicted_gender}")
#     print(f"Predicted Emotion Index: {predicted_emotion}")

Real-Time Pipeline Example

For a complete application, integrate the model with an image preprocessing pipeline (face detection, alignment, normalization).

🚀 Deployment Options

Hardware Requirements

Environment	VRAM / RAM	Inference Speed	Recommended For
GPU (Optimal)	1-2 GB	⚡⚡⚡ Very Fast	Servers, real-time analysis systems
CPU (Efficient)	500 MB - 1 GB	⚡ Fast	Edge devices, kiosks, offline applications
Mobile (Converted)	< 500 MB	⚡ Medium	On-device mobile apps (requires conversion to ONNX/TFLite)

Suggested Deployment Stack

API Server: Wrap the model in a FastAPI or Flask server for RESTful endpoints.

Docker Container: Package dependencies for consistent deployment.

FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
WORKDIR /app
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
COPY . .
CMD ["python", "api_server.py"]

📁 Repository Structure

FaceVerifyAI-Advanced/
├── README.md                       # This file
├── best_advanced_face_verify_ai_model.pth # Main model weights

⚠️ Limitations & Ethical Considerations

Technical Limitations

Input Dependency: Accuracy is highly dependent on proper face detection, alignment, and lighting normalization in the input pipeline.
Dataset Scope: Trained on synthetic data; performance may vary on real-world images with extreme poses, occlusions, or uncommon demographics.
Emotion Classes: Recognizes a limited set (3) of fundamental emotions. Not a substitute for comprehensive psychological analysis.

Ethical Use & Bias

Inherent Bias: Like all AI models, it may reflect biases present in the training data. Comprehensive testing across diverse demographics is critical before deployment.
Privacy: Must be used in compliance with local privacy regulations (e.g., GDPR, CCPA). Users should be informed when their facial data is being processed.
Use Case Restriction: Not intended for high-stakes decision-making in legal, hiring, or security access control without human oversight and additional safeguards.

🔄 Version History

Version	Date	Key Updates
v1.0.0	2026-01-26	Initial public release of FaceVerifyAI-Advanced.

📄 License & Citation

License: Apache 2.0

Citation:

@misc{faceverifyai2026,
  title={FaceVerifyAI-Advanced: A Multi-Task Model for Facial Attribute Analysis},
  author={QuantaSparkLabs},
  year={2026},
  url={https://huggingface.co/QuantaSparkLabs/FaceVerifyAI-Advanced}
}

👥 Credits & Acknowledgments

Development & Training: QuantaSparkLabs AI Team.
Dataset Synthesis: Internal tools for generating structured synthetic face data.
Framework: Built with PyTorch.

🤝 Contributing & Support

Reporting Issues: Please open an issue on the Hugging Face model repository detailing the problem, your environment, and steps to reproduce.
Support: For questions, use the community discussion tab on the model page.

Built with ❤️ by QuantaSparkLabs
Model ID: FaceVerifyAI-Advanced • Release: 2026

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