Introduction
Related work
Head pose estimation
The model-based approach
The appearance-based approach
Random forests
Binary pattern
The local binary pattern
The centralized binary pattern
The local directional pattern
Gray level run length matrices
Proposed head pose estimation algorithm
Random forests framework
Training
Testing
Experiments
Algorithm | Raw image | LBP image | CBP image | LDP image |
---|---|---|---|---|
PCA + SVM | 64.6% | 69.0% | 70.3% | 75.9% |
LDA + SVM | 73.9% | 76.4% | 78.7% | 80.0% |
Proposed | 93.1% | - | - | - |
Algorithm | Class1 | Class2 | Class3 | Class4 | Class5 | Class6 | Class7 |
---|---|---|---|---|---|---|---|
PCA + LDP + SVM | 60.4% | 70.9% | 90.5% | 86.4% | 70.2% | 68.5% | 84.3% |
LDA + LDP + SVM | 62.4% | 84.0% | 92.1% | 74.5% | 80.4% | 86.7% | 80.6% |
Proposed | 91.3% | 90.6% | 96.8% | 88.7% | 92.7 | 93.9% | 97.2% |