A processor implemented iris region extraction method, the method comprising: obtaining an eye image; generating a lower-resolution image of the eye image with a resolution lower than a resolution of the eye image through image downsampling of the eye image; generating a classification map associated with the iris region from the generated lower-resolution image using a first trained neural network model, said classification map indicating for each pixel in the lower-resolution image whether it belongs to a pupil region, an iris region, or a background region; determining a plurality of refinement regions (942, 944, 946, 948, 950) in the eye image using the generated classification map and a geometric model associated with the iris region, wherein the geometric model includes two circles or ellipses to define the pupil region and the iris region, and curves to define shapes of an upper eyelid and/or a lower eyelid, wherein locations of the refinement regions are determined based on points at which straight lines passing through a central point of the pupil region in the classification map and a boundary of the pupil region meet and straight lines passing through the central point of the pupil region in the classification map and an outer boundary of the iris region in the classification map meet, wherein an inclination of each of the straight lines passing through the central point of the pupil region is predetermined based on the geometric model, such that the refinement regions in the eye image comprise refinement regions corresponding to the boundary of the pupil region, refinement regions (942, 944) corresponding to the outer boundary of the iris region, and refinement regions (946, 948) corresponding to a boundary of the iris region occluded by the upper eyelid or the lower eye lid; extracting pixels from the refinement regions (942, 944, 946, 948, 950) using a second trained neural network, wherein the second neural network classifies the pixels of the refinement regions to obtain a high-resolution classification map indicating which region of a pupil, an iris and a background, each pixel of the refinement regions belongs to; estimating one or more geometric parameters associated with the iris region based on the high-resolution classification map; and extracting the iris region from the eye image based on the estimated one or more geometric parameters. obtaining an eye image; generating a lower-resolution image of the eye image with a resolution lower than a resolution of the eye image through image downsampling of the eye image; generating a classification map associated with the iris region from the generated lower-resolution image using a first trained neural network model, said classification map indicating for each pixel in the lower-resolution image whether it belongs to a pupil region, an iris region, or a background region; determining a plurality of refinement regions (942, 944, 946, 948, 950) in the eye image using the generated classification map and a geometric model associated with the iris region, wherein the geometric model includes two circles or ellipses to define the pupil region and the iris region, and curves to define shapes of an upper eyelid and/or a lower eyelid, wherein locations of the refinement regions are determined based on points at which straight lines passing through a central point of the pupil region in the classification map and a boundary of the pupil region meet and straight lines passing through the central point of the pupil region in the classification map and an outer boundary of the iris region in the classification map meet, wherein an inclination of each of the straight lines passing through the central point of the pupil region is predetermined based on the geometric model, such that the refinement regions in the eye image comprise refinement regions corresponding to the boundary of the pupil region, refinement regions (942, 944) corresponding to the outer boundary of the iris region, and refinement regions (946, 948) corresponding to a boundary of the iris region occluded by the upper eyelid or the lower eye lid; extracting pixels from the refinement regions (942, 944, 946, 948, 950) using a second trained neural network, wherein the second neural network classifies the pixels of the refinement regions to obtain a high-resolution classification map indicating which region of a pupil, an iris and a background, each pixel of the refinement regions belongs to; estimating one or more geometric parameters associated with the iris region based on the high-resolution classification map; and extracting the iris region from the eye image based on the estimated one or more geometric parameters.