--- tags: - image-to-image - pytorch - computer-vision - face-verification license: apache-2.0 language: - en metrics: - accuracy library_name: transformers --- # 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. ## ๐Ÿ“‹ 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. ## โœจ Core Features | ๐ŸŽฏ Multi-Task Analysis | โšก 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. | ## ๐Ÿ“Š Performance Benchmarks ### ๐Ÿ† 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 ### ๐Ÿ”ฌ 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. ## ๐Ÿ—๏ธ Model Architecture ### High-Level Pipeline The architecture follows a streamlined, multi-head design: ``` Input Image (3xHxW) โ†“ [Shared CNN Backbone] โ†“ [Shared Fully Connected Layers] โ†“ โ”Œโ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ” โ†“ โ†“ โ†“ 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 โ†’ 256 units). * **Task Heads**: Separate linear output layers for age (1 neuron, regression), gender (2 neurons), and emotion (3 neurons) tasks. ## ๐Ÿ”ง 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) ## ๐Ÿ’ป Quick Start ### Installation Ensure you have PyTorch installed. The model requires only core libraries. ```bash 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. ```python 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("โœ… 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). ## ๐Ÿš€ Deployment Options ### Hardware Requirements | Environment | VRAM / RAM | Inference Speed | Recommended For | | :--- | :--- | :--- | :--- | | **GPU (Optimal)** | 1-2 GB | โšกโšกโšก Very Fast | Servers, real-time analysis systems | | **CPU (Efficient)** | 500 MB - 1 GB | โšก Fast | Edge devices, kiosks, offline applications | | **Mobile (Converted)** | < 500 MB | โšก 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. ```dockerfile 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"] ``` ## ๐Ÿ“ Repository Structure ``` FaceVerifyAI-Advanced/ โ”œโ”€โ”€ README.md # This file โ”œโ”€โ”€ best_advanced_face_verify_ai_model.pth # Main model weights ``` ## โš ๏ธ 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. ## ๐Ÿ”„ Version History | Version | Date | Key Updates | | :--- | :--- | :--- | | v1.0.0 | 2026-01-26 | Initial public release of FaceVerifyAI-Advanced. | ## ๐Ÿ“„ License & Citation **License:** Apache 2.0 **Citation:** ```bibtex @misc{faceverifyai2026, title={FaceVerifyAI-Advanced: A Multi-Task Model for Facial Attribute Analysis}, author={QuantaSparkLabs}, year={2026}, url={https://huggingface.co/QuantaSparkLabs/FaceVerifyAI-Advanced} } ``` ## ๐Ÿ‘ฅ Credits & Acknowledgments * **Development & Training**: QuantaSparkLabs AI Team. * **Dataset Synthesis**: Internal tools for generating structured synthetic face data. * **Framework**: Built with PyTorch. ## ๐Ÿค 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 โค๏ธ by QuantaSparkLabs
Model ID: FaceVerifyAI-Advanced โ€ข Release: 2026