A computer-implemented liveness test method comprising: detecting an object area comprising an object in an input image (310, 410, 710); determining a pre-liveness score (320, 470) of the object based on sub-images (415,425,435) acquired from the input image, comprising: acquiring different sub-images (415, 425, 435) from the input image based on the object included in the input image, wherein the sub-images (415, 425, 435) are based on areas of the input image (310,410); determining liveness scores corresponding to the sub-images (415, 425, 435) using respective liveness test models; determining the pre-liveness score (320, 470) further comprising applying a weight to one or more of the determined liveness scores, and determining the pre-liveness score based on a weighted average or an arithmetic average of the weighted determined liveness scores; recognizing the object included in the input image using a recognition model comprising a neural network; determining a post-liveness score (350, 740) of the object based on the recognition result, comprising: determining further liveness scores from feature vectors output from a plurality of hidden layers within the recognition model; determining the post-liveness score by using a weighted average or an arithmetic average of the further liveness scores; determining a liveness (330, 350) of the object based on the pre-liveness score and the post-liveness score. detecting an object area comprising an object in an input image (310, 410, 710); determining a pre-liveness score (320, 470) of the object based on sub-images (415,425,435) acquired from the input image, comprising: acquiring different sub-images (415, 425, 435) from the input image based on the object included in the input image, wherein the sub-images (415, 425, 435) are based on areas of the input image (310,410); determining liveness scores corresponding to the sub-images (415, 425, 435) using respective liveness test models; acquiring different sub-images (415, 425, 435) from the input image based on the object included in the input image, wherein the sub-images (415, 425, 435) are based on areas of the input image (310,410); determining liveness scores corresponding to the sub-images (415, 425, 435) using respective liveness test models; determining the pre-liveness score (320, 470) further comprising applying a weight to one or more of the determined liveness scores, and determining the pre-liveness score based on a weighted average or an arithmetic average of the weighted determined liveness scores; recognizing the object included in the input image using a recognition model comprising a neural network; determining a post-liveness score (350, 740) of the object based on the recognition result, comprising: determining further liveness scores from feature vectors output from a plurality of hidden layers within the recognition model; determining the post-liveness score by using a weighted average or an arithmetic average of the further liveness scores; determining further liveness scores from feature vectors output from a plurality of hidden layers within the recognition model; determining the post-liveness score by using a weighted average or an arithmetic average of the further liveness scores; determining a liveness (330, 350) of the object based on the pre-liveness score and the post-liveness score.