Discriminative Robust Gaze Estimation Using Kernel-DMCCA Fusion

The proposed framework employs discriminative analysis for gaze estimation using kernel discriminative multiple canonical correlation analysis (K-DMCCA), which represents different feature vectors that account for variations of head pose, illumination and occlusion. The feature extraction component of the framework includes spatial indexing, statistical and geometrical elements. Gaze estimation is constructed by feature aggregation and transforming features into a higher dimensional space using the RBF kernel γ and spread factor. The output of fused features through K-DMCCA is robust to illumination, occlusion and is calibration free. Our algorithm is validated on MPII, CAVE, ACS and EYEDIAP datasets. The two main contributions of the framework are the following: Enhancing the performance of DMCCA with the kernel and introducing quadtree as an iris region descriptor. Spatial indexing using quadtree is a robust method for detecting which quadrant the iris is situated, detecting the iris boundary and it is inclusive of statistical and geometrical indexing that are calibration free. Our method achieved an accurate gaze estimation of 4.8° using Cave, 4.6° using MPII, 5.1° using ACS and 5.9° using EYEDIAP datasets respectively. The proposed framework provides insight into the methodology of multi-feature fusion for gaze estimation.