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Gait Biometric Identification System Complete Documentation

1. Introduction

This project implements a state-of-the-art gait biometric identification system with an integrated Gradient Reversal Layer (GRL) for view-invariant learning.

Key Features

• Architecture: Set-based deep learning model for gait recognition
• Gradient Reversal Layer (GRL): Domain adaptation for view-invariant features
• Multi-Device Support: CUDA (NVIDIA), MPS (Apple Silicon), and CPU
• Comprehensive Evaluation: Rank-k accuracy, mAP, and CMC curves
• Gallery-Probe Split: Standard evaluation protocol for CASIA-B dataset
• Flexible Configuration: YAML-based configuration system

What is Gait Recognition?

Gait recognition identifies individuals based on their walking patterns. Unlike face or fingerprint recognition, gait can be captured from a distance without subject cooperation, making it valuable for surveillance and security applications.

Why GRL?

Traditional gait recognition models struggle when the view angle (camera position) during testing differs from training. The Gradient Reversal Layer solves this by forcing the model to learn features that are:

• Discriminative for identity classification
• Invariant to view angles

---

2. System Architecture

High-Level Overview

Input Silhouettes
      ↓
[Data Loading & Preprocessing]
      ↓
[Backbone Network]
      ↓
[Feature Embeddings] ──→ [Identity Classifier]
      ↓                         ↓
[GRL (Optional)]          [Identity Loss]
      ↓
[View Discriminator]
      ↓
[View Loss]

Component Interaction

1. Data Pipeline:

   • Loads silhouette sequences from CASIA-B dataset
   • Applies augmentation (flip, rotation, erasing)
   • Organizes batches for triplet loss training

2. Feature Extraction:

   • CNN backbone extracts frame-level features
   • Horizontal Pyramid Pooling aggregates spatial information
   • Temporal pooling creates sequence-level representation

3. Multi-Task Learning:

   • Identity Classification: Recognize who is walking
   • View Invariance (via GRL): Learn features independent of camera angle
   • Metric Learning: Ensure same-person samples are similar

4. Evaluation:

   • Gallery-probe matching
   • Rank-k accuracy and mAP computation
   • Cross-view and cross-condition evaluation

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3. Network Architecture

3.1 Backbone

Purpose: Extract discriminative gait features from silhouette sequences.

Architecture:

Input: [Batch, Frames, Height, Width]
      ↓
┌─────────────────────────────────┐
│  Frame-Level Feature Extraction │
│  ┌───────────────────────────┐  │
│  │ Conv2D + BN + LeakyReLU   │  │  32 channels
│  │ Conv2D + BN + LeakyReLU   │  │  32 channels
│  │ MaxPool2D (stride=2)      │  │
│  ├───────────────────────────┤  │
│  │ Conv2D + BN + LeakyReLU   │  │  64 channels
│  │ Conv2D + BN + LeakyReLU   │  │  64 channels
│  │ MaxPool2D (stride=2)      │  │
│  ├───────────────────────────┤  │
│  │ Global-Local Conv (128)   │  │  Multi-scale features
│  │ Global-Local Conv (128)   │  │
│  └───────────────────────────┘  │
└─────────────────────────────────┘
      ↓
┌─────────────────────────────────┐
│  Horizontal Pyramid Pooling     │
│  ┌───────────────────────────┐  │
│  │ Bin 16: 16 strips         │  │
│  │ Bin 8:  8 strips          │  │
│  │ Bin 4:  4 strips          │  │
│  │ Bin 2:  2 strips          │  │
│  │ Bin 1:  1 strip           │  │
│  │ GeM Pooling per strip     │  │
│  │ Concatenate all strips    │  │
│  └───────────────────────────┘  │
└─────────────────────────────────┘
      ↓
┌─────────────────────────────────┐
│  Temporal Pooling               │
│  Max Pooling + Mean Pooling     │
└─────────────────────────────────┘
      ↓
┌─────────────────────────────────┐
│  Fully Connected Layers         │
│  FC(hidden_dim) + BN + ReLU     │
│  Dropout(0.5)                   │
│  FC(embedding_dim)              │
└─────────────────────────────────┘
      ↓
Output: [Batch, Embedding_Dim]

Key Components:

1. Global-Local Convolution (GLConv):

   Local Branch:  3x3 Conv
   Global Branch: 3x3 Conv → MaxPool → 3x3 Conv → Upsample
   Combine: Local + Global
   

   • Captures both fine-grained and coarse features
   • Improves robustness to scale variations

2. Generalized Mean Horizontal Pyramid Pooling (GeMHPP):

   For each pyramid level (16, 8, 4, 2, 1 strips):
       For each horizontal strip:
           Apply GeM pooling: (∑ x^p)^(1/p)
       Concatenate strip features

   • Multi-scale spatial aggregation
   • Learnable pooling parameter p
   • Captures both global structure and local details

3. Temporal Pooling:

   Max Pooling: max(features, dim=time)
   Mean Pooling: mean(features, dim=time)
   Concatenate: [max_features, mean_features]

   • Set-based: Order-invariant
   • Handles variable sequence lengths
   • Combines complementary statistics

3.2 Gradient Reversal Layer (GRL)

Purpose: Learn view-invariant features through adversarial training.

Mathematical Formulation:

The GRL implements the following optimization:

$$ \min_{\theta_f, \theta_y} \max_{\theta_d} \mathcal{L}(\theta_f, \theta_y, \theta_d) $$

where:

• $\theta_f$: Feature extractor parameters
• $\theta_y$: Identity classifier parameters
• $\theta_d$: Domain (view) discriminator parameters

$$ \mathcal{L} = \mathcal{L}_{\text{identity}}(y, \hat{y}) - \lambda \mathcal{L}_{\text{view}}(v, \hat{v}) $$

Implementation:

# Forward pass: Identity transform
y = GRL(x)  # y = x

# Backward pass: Gradient reversal
dx = -λ * dy

How It Works:

1. Forward Pass:

   Features → [GRL] → View Discriminator → View Prediction
   

   • GRL acts as identity (features pass through unchanged)

2. Backward Pass:

   View Loss Gradient → [GRL] → Reversed Gradient → Feature Extractor
   

   • GRL multiplies gradient by -λ
   • Feature extractor receives reversed gradients
   • This forces it to learn view-invariant features

3. Training Dynamics:

   • View Discriminator: Tries to classify view angle from features
   • Feature Extractor: Tries to confuse the discriminator
   • Result: Features that work well for all views

Lambda Scheduling:

# Constant: λ = λ_max
lambda = 1.0

# Progressive: Gradually increase from 0 to λ_max
p = epoch / max_epochs
lambda = (2 / (1 + exp(-10 * p)) - 1) * lambda_max

3.3 View Discriminator

Architecture:

Input: [Batch, Embedding_Dim]
      ↓
FC(256) → BN → ReLU → Dropout(0.3)
      ↓
FC(128) → BN → ReLU → Dropout(0.3)
      ↓
FC(64) → BN → ReLU → Dropout(0.3)
      ↓
FC(num_views=11)
      ↓
Output: [Batch, 11] (view angle logits)

Purpose: Classify view angle from features (to be confused by GRL).

---

4. Training Pipeline

4.1 Overall Training Flow

for epoch in range(num_epochs):
    # 1. Update GRL lambda
    model.update_grl_lambda(epoch, num_epochs)
    
    # 2. Train one epoch
    for batch in train_loader:
        # Load data
        silhouettes, subject_ids, view_angles = batch
        
        # Forward pass
        embeddings, identity_logits = model(silhouettes)
        
        # Apply GRL (if enabled)
        view_logits = grl_module(embeddings)
        
        # Compute losses
        identity_loss = CrossEntropy(identity_logits, subject_ids)
        triplet_loss = TripletLoss(embeddings, subject_ids)
        view_loss = CrossEntropy(view_logits, view_angles)
        center_loss = CenterLoss(embeddings, subject_ids)
        
        total_loss = (w1 * identity_loss + 
                     w2 * triplet_loss + 
                     w3 * center_loss + 
                     w4 * view_loss)
        
        # Backward pass
        optimizer.zero_grad()
        total_loss.backward()
        clip_grad_norm(model.parameters(), max_norm=5.0)
        optimizer.step()
    
    # 3. Learning rate scheduling
    scheduler.step()
    
    # 4. Evaluation
    if epoch % eval_freq == 0:
        evaluate(model, gallery_loader, probe_loader)
    
    # 5. Save checkpoint
    save_checkpoint(model, optimizer, epoch)

4.2 Loss Functions

4.2.1 Identity Classification Loss

L_identity = CrossEntropyLoss(identity_logits, subject_ids)

• Purpose: Direct supervision for identity recognition
• Label Smoothing: Prevents overconfidence

  CrossEntropyLoss(label_smoothing=0.1)

4.2.2 Triplet Loss (Batch Hard Mining)

For each anchor in batch:
    hardest_positive = max(distance(anchor, positive))
    hardest_negative = min(distance(anchor, negative))
    
    loss = max(hardest_positive - hardest_negative + margin, 0)

L_triplet = mean(loss)

• Purpose: Metric learning - ensure same-person samples are closer
• Batch Hard Mining: Select most difficult triplets
• Margin: Minimum separation between positive and negative pairs

Distance Computation:

# Euclidean distance
distance = sqrt(sum((a - b)^2))

# Cosine distance
distance = 1 - (a · b) / (||a|| * ||b||)

4.2.3 Center Loss

L_center = (1/2) * sum(||embeddings - centers[labels]||^2)

# Center update
centers[label] = centers[label] - alpha * (centers[label] - mean(embeddings))

• Purpose: Intra-class compactness
• Effect: Pulls samples toward their class center
• Benefits: Reduces intra-class variance

4.2.4 View Classification Loss (GRL)

L_view = CrossEntropyLoss(view_logits, view_labels)

• Purpose: Adversarial training for view invariance
• Note: Gradient is reversed by GRL before reaching feature extractor

4.3 Batch Sampling Strategy

Triplet Sampler:

Batch Structure:
- P persons per batch (e.g., 8)
- K samples per person (e.g., 16)
- Total batch size = P × K = 128

Sampling Process:
1. Randomly select P persons
2. For each person:
   - Sample K sequences (different views/conditions)
3. Construct batch of P×K samples

Why This Strategy?:

• Ensures each batch contains multiple samples from same identity
• Enables effective triplet mining within batch
• Improves metric learning efficiency

---

5. Evaluation Pipeline

5.1 Gallery-Probe Protocol

CASIA-B Standard Protocol:

Training Set:
- Subjects: 001-098 (98 subjects)
- Conditions: nm-01 to nm-06 (normal walking)
- Views: All 11 views (0°-180°)

Test Set (Gallery):
- Subjects: 099-124 (26 subjects)
- Conditions: nm-05, nm-06
- Views: All 11 views

Test Set (Probe):
- Subjects: 075-124 (same 50 subjects)
- Conditions:
  * Normal: nm-01, nm-02, nm-03, nm-04
  * Bag: bg-01, bg-02
  * Clothing: cl-01, cl-02
- Views: All 11 views

Evaluation Process:

# 1. Extract features
gallery_features = extract_features(gallery_loader)
probe_features = extract_features(probe_loader)

# 2. Compute distance matrix
distance_matrix = compute_distances(probe_features, gallery_features)
# Shape: [num_probe, num_gallery]

# 3. For each probe sample
for i, probe in enumerate(probes):
    # Sort gallery by distance (ascending)
    ranking = argsort(distance_matrix[i])
    
    # Check if correct match appears in top-k
    correct_label = probe_labels[i]
    correct_positions = where(gallery_labels[ranking] == correct_label)
    
    # Update metrics
    if first_correct_position < k:
        rank_k_correct += 1

5.2 Evaluation Metrics

5.2.1 Rank-k Accuracy

Definition: Percentage of queries where the correct match appears in the top-k retrieved samples.

Rank-k = (Number of queries with correct match in top-k) / (Total queries) × 100%

Common Values:

• Rank-1: Most important - is the top match correct?
• Rank-5: Is the correct match in top 5?
• Rank-10: Is the correct match in top 10?

5.2.2 Mean Average Precision (mAP)

Average Precision (AP) for a single query:

AP = (sum of (Precision at k × relevance(k))) / (number of relevant items)

where:
- Precision at k = (correct matches in top k) / k
- relevance(k) = 1 if item k is relevant, 0 otherwise

Mean Average Precision:

mAP = mean(AP across all queries)

Interpretation: mAP considers the ranking quality, not just whether correct match is in top-k.

5.2.3 Cumulative Match Characteristic (CMC) Curve

Definition: Plot of Rank-k accuracy vs. k.

for k in range(1, max_rank + 1):
    CMC[k] = Rank-k Accuracy

Visualization:

Recognition Rate (%)
100%|                    _______________
    |                __/
    |             __/
 50%|         __/
    |     __/
  0%|___/________________________
     1    5    10    20    50   Rank

5.3 Cross-View Evaluation

Purpose: Evaluate robustness to view angle changes.

For each gallery view angle (e.g., 0°, 18°, ...):
    For each probe view angle:
        Compute metrics
        Store results in matrix

# Results matrix
results[gallery_view][probe_view] = rank1_accuracy

Expected Behavior:

• Without GRL: Performance drops significantly when views differ
• With GRL: More consistent performance across view pairs

---

6. File Structure

gait_biometric_identification/
│
├── configs/
│   └── config.yaml                    # Main configuration file
│
├── data/
│   ├── __init__.py                    # Data module exports
│   ├── dataset.py                     # CASIA-B dataset loader
│   ├── transforms.py                  # Data augmentation
│   └── sampler.py                     # Triplet batch sampler
│
├── models/
│   ├── __init__.py                    # Model module exports
│   ├── backbone.py                    # Backbone architecture
│   ├── grl.py                         # Gradient Reversal Layer
│   ├── gait_model.py                  # Complete gait recognition model
│   └── losses.py                      # Loss functions
│
├── utils/
│   ├── __init__.py                    # Utils module exports
│   ├── device.py                      # Device management & seeding
│   ├── metrics.py                     # Evaluation metrics
│   └── visualization.py               # Plotting functions
│
├── scripts/
│   ├── train.sh                       # Training shell script
│   └── evaluate.py                    # Evaluation script
│
├── train.py                           # Main training script
├── requirements.txt                   # Python dependencies
├── wiki.md                            # This documentation
└── README.md                          # Quick start guide

6.1 Key File Descriptions

configs/config.yaml

• Central configuration for all hyperparameters
• Dataset paths and split settings
• Model architecture parameters
• GRL enable/disable toggle
• Training and evaluation settings
• Device configuration (CUDA/MPS/CPU)

data/dataset.py

• CASIABDataset: PyTorch Dataset for CASIA-B
• Handles pickle file loading
• Implements frame sampling strategies
• Manages gallery/probe splits
• Supports data caching

data/transforms.py

• GaitTransform: Augmentation pipeline
• Resizing to target resolution
• Horizontal flipping
• Random rotation and erasing

data/sampler.py

• TripletSampler: Batch sampler for triplet loss
• Ensures P persons × K samples per batch
• Enables efficient triplet mining

models/backbone.py

• SetBlock: Basic conv block
• GLConv: Global-Local convolution
• GeMHPP: Horizontal pyramid pooling
• TemporalPooling: Set-based aggregation

models/grl.py

• GradientReversalFunction: Custom autograd function
• GradientReversalLayer: GRL module
• ViewDiscriminator: View angle classifier
• DomainAdaptationModule: Complete GRL pipeline

models/gait_model.py

• GaitRecognitionModel: Complete end-to-end model
• Combines backbone + GRL + classifiers
• Handles training/evaluation modes
• Supports GRL toggle

models/losses.py

• TripletLoss: Batch hard triplet loss
• CenterLoss: Intra-class compactness
• CombinedLoss: Multi-task loss aggregation

utils/metrics.py

• compute_distance_matrix: Pairwise distances
• evaluate_rank: Rank-k accuracy
• compute_cmc: CMC curve
• compute_map: Mean Average Precision
• AverageMeter: Training metric tracking

utils/device.py

• get_device: Auto-detect best device
• setup_seed: Set random seeds for reproducibility
• print_system_info: Display environment info

utils/visualization.py

• plot_cmc_curve: CMC visualization
• plot_tsne: Feature space visualization
• plot_confusion_matrix: Confusion matrix
• plot_training_curves: Training progress

train.py

• Main training script
• Implements training loop
• Handles checkpointing
• Supports resume from checkpoint
• Periodic evaluation

scripts/evaluate.py

• Standalone evaluation script
• Loads trained model
• Comprehensive evaluation on test set
• Generates visualizations

---

7. Usage Instructions

7.1 Installation

# Clone or navigate to project directory
cd gait_biometric_identification

# Install dependencies
pip install -r requirements.txt

7.2 Data Preparation

Ensure CASIA-B dataset is organized as:

CASIA-B/casiab-128-end2end/
├── 001/
│   ├── nm-01/
│   │   ├── 000/
│   │   │   ├── 000-sils.pkl
│   │   │   └── ...
│   │   ├── 018/
│   │   └── ...
│   ├── nm-02/
│   ├── bg-01/
│   ├── cl-01/
│   └── ...
├── 002/
└── ...

Update config.yaml with your dataset path:

dataset:
  data_root: "/path/to/CASIA-B/casiab-128-end2end"

7.3 Training

Option 1: Using shell script

cd scripts
./train.sh

Option 2: Direct Python command

python train.py --config configs/config.yaml

Training with custom config

python train.py --config configs/my_config.yaml

Resume from checkpoint

python train.py \
    --config configs/config.yaml \
    --resume output/checkpoint_epoch_50.pth

7.4 Evaluation

Evaluate trained model

python scripts/evaluate.py \
    --config configs/config.yaml \
    --checkpoint output/best_model.pth \
    --output_dir evaluation_results \
    --visualize

Evaluation only (no training)

python train.py \
    --config configs/config.yaml \
    --resume output/best_model.pth \
    --eval_only

7.5 Monitoring Training

TensorBoard

tensorboard --logdir output/tensorboard

Then open http://localhost:6006 in your browser.

Metrics displayed:

• Training loss (total, identity, triplet, center, view)
• Learning rate
• Validation metrics (Rank-1, Rank-5, mAP)

---

8. Technical Details

8.1 Mathematical Foundations

8.1.1 Set-Based Learning

Traditional sequence models (RNN, LSTM) assume temporal order matters. For gait, the order of frames is less important than the overall pattern.

Set Function Properties:

f(S) = f(permute(S))  # Order-invariant
f(S ∪ {x}) depends on f(S) and x  # Permutation-equivariant per-element processing

Implementation:

# Process each frame independently
frame_features = CNN(frames)  # [N×T, C, H, W]

# Aggregate using symmetric function
max_pool = max(frame_features, dim=time)
mean_pool = mean(frame_features, dim=time)
set_features = concat([max_pool, mean_pool])

8.1.2 Metric Learning Objective

Goal: Learn embedding space where:

d(x_i, x_j) < d(x_i, x_k)  if y_i = y_j and y_i ≠ y_k

Triplet Loss:

L = max(||f(a) - f(p)||² - ||f(a) - f(n)||² + α, 0)

where:
- a: anchor
- p: positive (same identity)
- n: negative (different identity)
- α: margin

Batch Hard Mining:

# For each anchor
for a in anchors:
    # Hardest positive: furthest same-identity sample
    p_hard = argmax(distance(a, positives))
    
    # Hardest negative: closest different-identity sample
    n_hard = argmin(distance(a, negatives))
    
    loss += max(d(a, p_hard) - d(a, n_hard) + margin, 0)

8.1.3 Domain Adaptation via GRL

Standard Training:

min L_task(θ)  # Minimize task loss

With Domain Adaptation:

min L_task(θ_f, θ_y) + λ L_domain(θ_f, θ_d)
    θ_f, θ_y

max L_domain(θ_f, θ_d)
    θ_d

Unified via GRL:

min L_task(θ_f, θ_y) - λ L_domain(θ_f, θ_d)
    θ_f, θ_y, θ_d

The negative sign is implemented by reversing gradients:

∂L/∂θ_f = ∂L_task/∂θ_f - λ ∂L_domain/∂θ_f

8.2 Implementation Optimizations

8.2.1 Memory Efficiency

Data Caching:

if cache_enabled:
    # Load all data into RAM at start
    self._cache = {}
    for pkl_path in data_files:
        self._cache[str(pkl_path)] = load_pickle(pkl_path)

Benefits:

• Avoid repeated disk I/O
• Faster epoch iteration
• Trade memory for speed

When to Use:

• Dataset fits in RAM (~15-20GB for CASIA-B)
• Multiple epochs of training
• Fast GPU training bottlenecked by I/O

8.2.2 Mixed Precision Training

Can be enabled for faster training on modern GPUs:

from torch.cuda.amp import autocast, GradScaler

scaler = GradScaler()

for batch in dataloader:
    with autocast():  # Mixed precision forward pass
        outputs = model(inputs)
        loss = criterion(outputs, targets)
    
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

Benefits:

• 2-3x faster training
• Reduced memory usage
• Minimal accuracy impact

8.2.3 DataLoader Optimization

DataLoader(
    dataset,
    batch_size=128,
    num_workers=8,  # Parallel data loading
    pin_memory=True,  # Faster GPU transfer
    prefetch_factor=2,  # Pre-load batches
    persistent_workers=True,  # Keep workers alive
)

8.3 Debugging and Troubleshooting

Common Issues

Issue 1: CUDA Out of Memory

RuntimeError: CUDA out of memory

Solutions:

• Reduce batch size (person_num or sample_num)
• Reduce frame_num
• Disable data caching
• Use gradient accumulation:

  for i, batch in enumerate(dataloader):
      loss = model(batch) / accumulation_steps
      loss.backward()
      
      if (i + 1) % accumulation_steps == 0:
          optimizer.step()
          optimizer.zero_grad()

Issue 2: NaN Loss

Loss becomes NaN after few iterations

Solutions:

• Reduce learning rate
• Enable gradient clipping (already enabled by default)
• Check for division by zero in custom code
• Verify data normalization

Issue 3: Poor Rank-1 Accuracy

Possible Causes:

• Insufficient training epochs
• Learning rate too high/low
• GRL lambda too high (overpowering identity loss)
• Data loading issues (verify samples are correct identities)

Debugging Steps:

# 1. Check if model can overfit small subset
# Train on 10 subjects only - should reach 100% accuracy

# 2. Visualize features with t-SNE
# Should see clear clusters per identity

# 3. Print distance statistics
pos_distances = distances_within_identity
neg_distances = distances_between_identities
print(f"Positive: {pos_distances.mean()}")
print(f"Negative: {neg_distances.mean()}")
# Negative should be > Positive + margin

---

9. Advanced Topics

9.1 Extending the System

Adding New Loss Functions

# In models/losses.py

class ContrastiveLoss(nn.Module):
    def __init__(self, margin=0.5):
        super().__init__()
        self.margin = margin
    
    def forward(self, embeddings, labels):
        # Implement contrastive loss
        distances = compute_pairwise_distances(embeddings)
        is_positive = (labels.unsqueeze(0) == labels.unsqueeze(1))
        
        pos_loss = (distances * is_positive).mean()
        neg_loss = (torch.clamp(self.margin - distances, min=0) * ~is_positive).mean()
        
        return pos_loss + neg_loss

Adding New Backbones

# In models/backbone.py

class GaitGL(nn.Module):
    """GaitGL: Another gait recognition architecture"""
    
    def __init__(self, ...):
        # Implement GaitGL architecture
        pass
    
    def forward(self, x):
        # Forward pass
        pass

9.2 Hyperparameter Tuning

Key Hyperparameters:

Hyperparameter	Impact	Recommended Range
Learning Rate	Convergence speed	1e-5 to 1e-3
Batch Size (P×K)	Gradient stability	64-256
GRL Lambda	View invariance	0.5-2.0
Triplet Margin	Embedding separation	0.1-0.5
Frame Number	Sequence information	20-40
Embedding Dim	Capacity	128-512

Tuning Strategy:

1. Start with learning rate:

   lrs = [1e-5, 1e-4, 1e-3]
   # Train for 10 epochs each, pick best

2. Tune batch composition:

   # Try different P×K combinations
   (P=4, K=32), (P=8, K=16), (P=16, K=8)
   # Keep total batch size constant

3. Optimize GRL lambda:

   lambdas = [0.0, 0.5, 1.0, 1.5, 2.0]
   # Evaluate cross-view performance

9.3 Cross-Dataset Evaluation

To evaluate generalization to other datasets:

# 1. Train on CASIA-B
python train.py --config configs/casiab_config.yaml

# 2. Evaluate on different dataset (e.g., OU-MVLP)
# Modify config to point to new dataset

python scripts/evaluate.py \
    --config configs/oumvlp_config.yaml \
    --checkpoint output/casiab_best_model.pth

---

10. Datasets

• CASIA-B: http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp
• OU-MVLP: http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html

---

11. Conclusion

This system implements a complete, production-ready gait biometric identification pipeline with:

✓ State-of-the-art architecture
✓ Optional GRL for view-invariant learning
✓ Comprehensive training and evaluation tools
✓ Multi-device support (CUDA/MPS/CPU)
✓ Extensive documentation and examples

Next Steps:

1. Train baseline model without GRL
2. Train model with GRL
3. Compare cross-view performance
4. Experiment with different hyperparameters
5. Evaluate on real-world scenarios

For questions or issues, refer to the troubleshooting section or examine the detailed code comments.

---