Research Article
An Efficient Robust Eye Localization by Learning the Convolution Distribution Using Eye Template
Table 2
Comparison of accuracies among the state-of-the-art eye localization approaches. Note the robust algorithms with high accuracies on LFPW.
| Method | Accuracy | Mean error | BioID | LFPW | BioID | LFPW | Right eye | Left eye | Right eye | Left eye | Right eye | Left eye | Right eye | Left eye |
| Our method | 98.1% | 98.2% | 96.8% | 96.9% | 2.7% | 2.4% | 3.4% | 3.1% | Deep CNN | 99.9% | 100% | 99.1% | 99.4% | 1.7% | 1.5% | 2.1% | 2.0% | ASEF | 1.2% | 0.2% | 2.4% | 0.6% | 121.4% | 88% | 81.2% | 99.2% | nu-SVR | 96.1% | 95.9% | 92.8% | 92.8% | 4.2% | 4.1% | 4.9% | 4.9% | BORMAN | 79.1% | 75.8% | 78.2% | 92.8% | 7.1% | 7.8% | 7.8% | 8.8% | CBDS | 97.7% | 98.9% | 87.9% | 91.9% | 4.1% | 3.9% | 7.2% | 7% | LUXAND | 98.9% | 98.66% | 95.6% | 96.8% | 4.1% | 3.7% | 5.6% | 4.5% |
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