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Table 1 Characteristics and performance of eye-gaze tracking systems and methods

From: A free geometry model-independent neural eye-gaze tracking system

Cameras Lights Calibration Approach (mapping function) Accuracy Reference Comments/Notes
1, 20 fps, 640×480 1 (+1) screen divided in 2×4 zones MI (2 GRNNs) 5° H 8° V [8]abc Two concentric IR light rings are alternately turned on and off. Only one glint is used.
1, 30 fps, 640×480 1 (0) 12×16 grid MI (1 MFNN) 2.4° H 2.4° V [17]bc Special spectacles frame is needed; both the eyes are used. Accuracy is measured on testing points.
1 low resol. 1 cursor moves MI (2 MFNNs) 1.5° [16]abc Accuracy is measured on testing points.
1, 30 fps, 640×480 2 3×3 grid MB 0.9° [9]ac  
2 2   MB 0.68° [9]ac Preliminary simulations
1, 60 fps, 640×480 2-4 1 point MB ≈ 0.7° H ≈ 0.7° V ≈ 1° [10]ac Each calibration point produces a gaze estimation model. 17 one-point calibrations were performed.
1, 60/120 fps, 640×480 4 (+1) 5 points NA 0.5° [22]ad Tracks both eyes simultaneously. Camera and the IR sources are built in the monitor.
1, 60/120 fps 1 (+1) 9 points NA 0.45°-0.70° [23]ad Typical and worst accuracy is reported. The POG estimation may require a dedicated computer.
1, 25 fps, 640×480 3 4×5 grid MI ≤ 0.41° H ≤ 0.49° V ≤ 0.62° E Proposed EGTSabc Worst accuracy bounds measured on halfway 3×4 testing grid for 3 different geometric settings.
  1. In the reference column, (a) Based on Pupil Corneal Reflection, (b) Mapping function based on artificial neural networks (c) System proposed in scientific literature, (d) Commercial system.