A processor implemented liveness test method comprising: receiving an input image; extracting an interest region of an object from the input image, wherein the interest region is a portion of the object in the input image comprising local texture information; generating a downscaled image by scaling down the input image, such that its size is the same as that of the interest region ; performing a liveness test on the object using a deep convolutional neural network model-based liveness test model , wherein the liveness test model: - uses the interest region as first input image information and the downscaled image as second input image information, both input to the liveness test model , wherein the first input image information of the interest region and the second input information of the downscaled image are input into the liveness test model through , respectively, an input layer for the first input image information of the interest region connected to a convolution layer and an input layer for the second input image information of the downscaled image connected to an other convolution layer 1 , wherein the convolutional layer and the other convolutional layer are connected to two different pooling layers, which are in turn connected to one convolutional layer; and - determines liveness of the object from the output value of the liveness test model ; and indicating a result of the liveness test. receiving an input image; extracting an interest region of an object from the input image, wherein the interest region is a portion of the object in the input image comprising local texture information; generating a downscaled image by scaling down the input image, such that its size is the same as that of the interest region ; performing a liveness test on the object using a deep convolutional neural network model-based liveness test model , wherein the liveness test model: - uses the interest region as first input image information and the downscaled image as second input image information, both input to the liveness test model , wherein the first input image information of the interest region and the second input information of the downscaled image are input into the liveness test model through , respectively, an input layer for the first input image information of the interest region connected to a convolution layer and an input layer for the second input image information of the downscaled image connected to an other convolution layer 1 , wherein the convolutional layer and the other convolutional layer are connected to two different pooling layers, which are in turn connected to one convolutional layer; and - determines liveness of the object from the output value of the liveness test model ; and - uses the interest region as first input image information and the downscaled image as second input image information, both input to the liveness test model , wherein the first input image information of the interest region and the second input information of the downscaled image are input into the liveness test model through , respectively, an input layer for the first input image information of the interest region connected to a convolution layer and an input layer for the second input image information of the downscaled image connected to an other convolution layer 1 , wherein the convolutional layer and the other convolutional layer are connected to two different pooling layers, which are in turn connected to one convolutional layer; and - determines liveness of the object from the output value of the liveness test model ; and indicating a result of the liveness test. <EOS>