A computer-implemented eye tracking method comprising: generating (S310) a reconstructed image by performing eye reconstruction with respect to an input image based on a principal component, wherein the generating (S310) comprises generating the reconstructed image based on a portion having a high priority among principal component vectors corresponding to the input image , and wherein each of the principal component vectors corresponds to an eigenface predetermined based on a principal component analysis on various face images; determining (S320) a difference value between at least one first pixel included in the input image and at least one second pixel included in the reconstructed image corresponding to the at least one first pixel; selecting a sample image having a highest similarity to the input image , among sample images stored in a database, as a replacement image , wherein the sample images correspond to images having previously succeeded in eye tracking; selecting (S340, S360, S380) one of the input image , the reconstructed image , and the replacement image as a target image based on the determined difference value; and performing eye tracking (S390) based on the target image, wherein the selecting comprises selecting (S340) the input image as the target image based on the determined difference value being less than a first threshold, selecting (S360) the reconstructed image as the target image based on the determined difference value being greater than the first threshold and less than a second threshold, and selecting (S380) the replacement image as the target image based on the determined difference value being greater than the second threshold, wherein the second threshold is greater than the first threshold. generating (S310) a reconstructed image by performing eye reconstruction with respect to an input image based on a principal component, wherein the generating (S310) comprises generating the reconstructed image based on a portion having a high priority among principal component vectors corresponding to the input image , and wherein each of the principal component vectors corresponds to an eigenface predetermined based on a principal component analysis on various face images; determining (S320) a difference value between at least one first pixel included in the input image and at least one second pixel included in the reconstructed image corresponding to the at least one first pixel; selecting a sample image having a highest similarity to the input image , among sample images stored in a database, as a replacement image , wherein the sample images correspond to images having previously succeeded in eye tracking; selecting (S340, S360, S380) one of the input image , the reconstructed image , and the replacement image as a target image based on the determined difference value; and performing eye tracking (S390) based on the target image, wherein the selecting comprises selecting (S340) the input image as the target image based on the determined difference value being less than a first threshold, selecting (S360) the reconstructed image as the target image based on the determined difference value being greater than the first threshold and less than a second threshold, and selecting (S380) the replacement image as the target image based on the determined difference value being greater than the second threshold, wherein the second threshold is greater than the first threshold. <EOS>