Automated Individualization of Deformable Eye Region Model and Its Application to Eye Motion Analysis

This paper proposes a method of automated individualization of eye region model. The eye region model has been proposed in past research that parameterizes both the structure and the motion of the eye region. Without any prior knowledge, one can never determine a given appearance of eye region to be either neutral to any expression, i.e., the inherent structure of the eye region, or the result of motion by a facial expression. The past method manually individualized the model with respect to the structure parameters in the initial frame and tracks the motion parameters automatically across the rest of the image sequence, assuming the initial frame contains only neutral faces. Under the same assumption, we automatically determine the structure parameters for the given eye region image. We train active appearance models (AAMs) for parameterizing the variance of individuality. The system projects a given eye region image onto the low dimensional subspace spanned by the AAM and retrieves the structure parameters of the nearest training sample and initializes the eye region model using them. The AAMs are trained in the subregions, i.e., the upper eyelid region, the palpebral fissure (the eye aperture) region, and the lower eyelid region, respectively. It enables each AAM to effectively represent fine structures. Experimental results show the proposed method gives as nice initialization as manual labor and allows comparative tracking results for a comprehensive set of eye motions.