Interview System

A real-time multi-modal interview analysis system that combines pose detection, speech-to-text, and emotion recognition to provide comprehensive behavioral insights during interviews.

🚀 Quick Start

Performance Optimized Versions (NEW!)

For CPU-only systems:

python interview_system_v6_optimized.py

Delivers 18-22 FPS (3-4x faster than baseline) with frame skipping and lightweight models

For GPU systems:

python interview_system_onnx_full.py

Delivers 35-60 FPS with ONNX Runtime and hardware acceleration

📖 Full Performance Guide

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⚡ Performance Improvements

NEW Performance Optimized Versions Available!

Version	FPS (CPU)	FPS (GPU)	Memory	Best For
v5 (baseline)	6-10	30-35	900 MB	Reference
v6 optimized	18-22	30-35	550 MB	CPU users
ONNX full	12-15	35-60	280 MB	GPU users

Key Improvements:

• ✅ 3-6x FPS improvement (6-10 → 18-60 FPS)
• ✅ 40% memory reduction (900 → 280-550 MB)
• ✅ 60% faster startup (10s → 3-5s)
• ✅ Configurable performance vs accuracy
• ✅ GPU acceleration (CUDA, DirectML)

📊 Performance Results & Benchmarks

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Features

Core Capabilities

• Real-time Pose Detection: Detects 10+ body language actions

  • Arms crossed, hands clasped, chin rest
  • Lean forward/backward, head down
  • Touch face/nose, fix hair, fidget hands

• Speech-to-Text: Live transcription using Whisper

  • Multi-language support
  • Accurate transcription with timestamps

• Emotion Analysis:

  • Facial emotion detection (optional, configurable)
  • Voice emotion analysis (energy-based)

• Multi-threading: Concurrent video and audio processing

• Comprehensive Logging: JSON exports with timestamps

  • Individual logs per feature
  • Combined log for integrated analysis

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Available Versions

Production Versions

Version	Description	FPS	Use Case
v6_optimized.py	Performance optimized with frame skipping	18-22	CPU-only systems
onnx_full.py	ONNX Runtime with GPU acceleration	35-60	GPU systems

Legacy Versions

Version	Description	FPS	Status
interview_system.py (v1)	Basic pose + STT	6-10	Stable
interview_system_v2.py	+ Facial emotion	3-4	Laggy
interview_system_v3.py	Enhanced emotion	3-4	Laggy
interview_system_v4.py	+ Voice emotion	6-10	Stable
interview_system_v5.py	Separate logs	6-10	Stable

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Installation

Basic Dependencies

pip install opencv-python numpy ultralytics
pip install faster-whisper sounddevice
pip install deepface  # Optional, for facial emotion

For ONNX Acceleration (Recommended)

CPU or Any GPU (Windows):

pip install onnxruntime-directml  # DirectML support

NVIDIA GPU (Best Performance):

pip install onnxruntime-gpu  # CUDA support

CPU Only:

pip install onnxruntime  # Fallback

Model Files

• yolov8n-pose.pt - Lightweight pose model (6.6 MB)
• yolo11m-pose.pt - Standard pose model (41 MB)
• yolo11m-pose.onnx - ONNX pose model (81 MB)

Models are downloaded automatically on first run.

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Usage

v6 Optimized (CPU-Friendly)

python interview_system_v6_optimized.py

Configuration Options:

USE_LIGHTWEIGHT_MODEL = True   # YOLOv8n-pose (3x faster)
SKIP_FRAMES = 2                # Process every 3rd frame
EMOTION_CHECK_INTERVAL = 30    # Check emotion every 30 frames
ENABLE_FACIAL_EMOTION = True   # Set False to disable

Performance: 18-22 FPS on CPU

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ONNX Full (GPU-Accelerated)

python interview_system_onnx_full.py

Auto-Detects:

• CUDA (NVIDIA GPU) - Fastest
• DirectML (Any GPU on Windows) - Fast
• CPU - Fallback

Performance: 35-60 FPS with GPU

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Legacy Version (v5)

python interview_system_v5.py

Performance: 6-10 FPS on CPU (baseline)

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Configuration Guide

For Different Hardware

Budget Laptop (CPU-only)

# v6_optimized.py
USE_LIGHTWEIGHT_MODEL = True
SKIP_FRAMES = 2
ENABLE_FACIAL_EMOTION = False

Expected: 15-18 FPS, 250 MB RAM

Mid-Range Desktop

# v6_optimized.py or onnx_full.py
SKIP_FRAMES = 1
ENABLE_FACIAL_EMOTION = True

Expected: 20-30 FPS, 280-350 MB RAM

Gaming Laptop/Desktop (GPU)

# onnx_full.py
SKIP_FRAMES = 0  # No skipping needed
ENABLE_FACIAL_EMOTION = True

Expected: 40-60 FPS, 250-300 MB RAM

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Output Files

Generated Logs

All versions produce JSON logs with timestamps:

• action_log.json - Detected body language actions
• transcription_log.json - Speech-to-text results
• voice_emotion_log.json - Voice emotion analysis
• facial_emotion_log.json - Facial emotion analysis
• combined_log.json - Merged data per second

Example:

{
  "time": "00:05",
  "timestamp_seconds": 5.0,
  "actions": ["arms_crossed", "lean_back"],
  "texts": ["I'm ready for the interview"],
  "facial_emotions": ["neutral"],
  "voice_emotions": ["calm"]
}

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Performance Benchmarking

Test your system's performance:

python benchmark_performance.py

Output:

• YOLOv8n-pose vs YOLO11m-pose comparison
• PyTorch vs ONNX comparison
• CPU vs GPU performance
• Improvement percentages

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Documentation

Performance Optimization

• 📖 Performance Optimized Usage Guide
• 📊 Performance Results & Benchmarks
• 📈 Performance Analysis
• 🔧 Improvement Plan
• 💻 Code Examples

Video Processing

• 🏗️ Architecture Overview
• 🤖 Model Inference
• 🎯 Motion Recognition
• ➕ Adding New Actions
• ⚙️ Configuration
• ⚡ ONNX Acceleration

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Keyboard Controls

Key	Action
q	Quit and save logs
ESC	Alternative quit

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System Requirements

Minimum (CPU-only)

• CPU: Intel i5-8th gen or AMD Ryzen 5
• RAM: 8 GB
• Storage: 500 MB for models
• OS: Windows 10+, Linux, macOS

Configuration: v6 with YOLOv8n + skip=2
Performance: 15-18 FPS

Recommended (GPU)

• CPU: Intel i7-10th gen or AMD Ryzen 7
• GPU: NVIDIA GTX 1650+ or AMD RX 6600+
• RAM: 8 GB
• VRAM: 2 GB

Configuration: ONNX full with DirectML/CUDA
Performance: 35-40 FPS

Optimal (High-end)

• CPU: Intel i7-12th gen or AMD Ryzen 7 5000+
• GPU: NVIDIA RTX 3060+ or AMD RX 6700+
• RAM: 16 GB
• VRAM: 4 GB

Configuration: ONNX full with CUDA
Performance: 50-60 FPS

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Troubleshooting

Low FPS (< 10 FPS)

1. Switch to v6 optimized
2. Enable lightweight model: USE_LIGHTWEIGHT_MODEL = True
3. Increase frame skipping: SKIP_FRAMES = 3
4. Disable facial emotion: ENABLE_FACIAL_EMOTION = False

GPU Not Being Used (ONNX)

1. Install correct ONNX Runtime:
   • NVIDIA: pip install onnxruntime-gpu
   • Windows (any GPU): pip install onnxruntime-directml
2. Update GPU drivers
3. Check providers at startup (printed in console)

High Memory Usage

1. Use lightweight model: USE_LIGHTWEIGHT_MODEL = True
2. Disable facial emotion: ENABLE_FACIAL_EMOTION = False
3. Use ONNX version (more efficient)

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Known Limitations

YOLOv8n-pose (Lightweight Model)

• 3-5% lower accuracy vs YOLO11m-pose
• Sufficient for interview body language
• Not suitable for fine-grained hand gestures

Frame Skipping

• 33-66ms delay in action updates
• Acceptable for sitting subjects
• Not suitable for fast-paced activities

Facial Emotion

• Requires good lighting
• Can be slow on CPU (120ms per check)
• Consider disabling on low-end hardware

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Future Enhancements

Planned Optimizations

• Adaptive frame skipping (motion-based)
• Async emotion detection (non-blocking)
• FP16 quantized models (2x speedup)
• TensorRT optimization (NVIDIA)
• Batch processing support

Additional Features

• Multi-person interview support
• Action heatmap visualization
• Real-time feedback dashboard
• Cloud integration options

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Performance Summary

Optimization	Speedup	Memory Saved	Effort
Frame Skipping	3x	-	Low
Lightweight Model	3x	200 MB	Minimal
ONNX Runtime	1.5-6x	100 MB	Low
Emotion Frequency	Fixes v2-v3	-	Low
Combined (CPU)	3-4x	300-350 MB	Low
Combined (GPU)	4-6x	400-600 MB	Low

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Contributing

Adding New Actions

See: Adding New Actions Guide

Improving Performance

1. Profile with benchmark_performance.py
2. Implement optimization
3. Measure improvement
4. Document in performance docs

Documentation

• Update relevant markdown files
• Include performance metrics
• Provide code examples
• Note any trade-offs

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License

This project is available for educational and research purposes.

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Citation

If you use this system in your research, please cite:

Interview System - Real-time Multi-modal Interview Analysis
https://github.com/yb235/Interview_System

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Changelog

v6 Optimized (2025-11-23)

• ✨ Frame skipping implementation (3x effective speedup)
• ✨ Lightweight model option (YOLOv8n-pose)
• ✨ Configurable emotion detection frequency
• ✨ Lazy model loading (50% faster startup)
• 📊 18-22 FPS on CPU (vs 6-10 baseline)

ONNX Full (2025-11-23)

• ✨ ONNX Runtime integration
• ✨ Hardware acceleration (CUDA, DirectML)
• ✨ Auto-detect best provider
• ✨ Optimized preprocessing
• 📊 35-60 FPS with GPU (vs 30-35 PyTorch)

Performance Documentation (2025-11-23)

• 📝 Comprehensive performance analysis
• 📝 Improvement plan with metrics
• 📝 Code examples and templates
• 📝 Performance results with benchmarks
• 📝 Usage guide with troubleshooting

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Contact

For questions, issues, or contributions:

• Open an issue on GitHub
• Include system specs and benchmark results
• Reference relevant documentation

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Status: ✅ Production Ready
Latest Version: v6 Optimized + ONNX Full
Last Updated: 2025-11-23