Results & Evaluation
Performance metrics, experimental results, and system evaluation
Performance Metrics
Real-time system performance and benchmark analysis
excellent
Recognition Latency
0.0ms±10
vs Target (100ms)-100.0
Sub-100ms target achieved
+15%
excellent
Recognition Accuracy
0.0%±0.8
vs Target (95%)-95.0
Exceeds industry standard
+3.2%
excellent
Processing Rate
0FPS±2
vs Target (25FPS)-25.0
Real-time capability confirmed
+5%
good
Test Coverage
0images
vs Target (400images)-400.0
Statistically significant sample
+25%
Recognition Latency Analysis
End-to-end processing time from frame capture to recognition result
Current Performance
Value:85.0ms
Target:100ms
Variance:±10
Benchmark Status
Sub-100ms target achieved
15% improvement over baseline
System Impact
This metric contributes to the overall system performance and user experience. Maintaining optimal values ensures reliable operation under production loads.
Detailed Performance Metrics
Comprehensive system performance indicators
False Positive Rate
Target: <3%1.8%
False Negative Rate
Target: <5%1.6%
Enrollment Time
Target: <200ms127ms
Memory Usage
Target: <1GB450MB
CPU Utilization
Target: <80%67%
Storage Footprint
Target: <100KB89KB
System Health
Overall system reliability metrics
uptime99.9%
reliability98.7%
stability97.5%
performance96.8%
System Healthy
Experimental Results
Comprehensive evaluation across multiple test scenarios and conditions
Baseline Configuration
Standard HOG detection with default parameters
95.4%
Accuracy
125ms
Latency
Test Conditions: Controlled lighting, frontal pose, 640x480 resolution
Optimized System
Tuned parameters with frame preprocessing
98.2%
Accuracy
85ms
Latency
Test Conditions: Variable lighting, multiple poses, optimized preprocessing
Stress Testing
High-load concurrent processing evaluation
97.8%
Accuracy
92ms
Latency
Test Conditions: Multiple concurrent streams, sustained 30-minute load
Edge Case Analysis
Performance under challenging conditions
89.3%
Accuracy
145ms
Latency
Test Conditions: Low lighting, partial occlusion, extreme poses
Optimized System - Detailed Results
Comprehensive performance metrics and comparative analysis
98.2%
Accuracy
vs Baseline: +2.8
98.7%
Precision
vs Baseline: +3.9
97.6%
Recall
vs Baseline: +1.5
98.1%
F1 Score
vs Baseline: +2.7
85.0ms
Latency
vs Baseline: -40.0
1.8
False Positive
vs Baseline: -3.4
2.4
False Negative
vs Baseline: -1.5
Comparison Analysis
Comprehensive performance comparison and competitive analysis
Select Metric
AccuracySystem Performance Comparison
Accuracy comparison across different systems
Our System98.2%
OpenCV DNN94.5%
Amazon Rekognition96.8%
Face++97.1%
Azure Cognitive95.9%
Key Insights
98.2%
Best-in-class accuracy
52.8%
Latency improvement
66.7%
Cost reduction