A processor implemented liveness test method comprising: receiving an input image; extracting an interest region (322, 324, 326, 328, 345, 370, 390, 420) of an object from the input image, wherein the interest region (322, 324, 326, 328, 345, 370, 390, 420) 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 (322, 324, 326, 328, 345, 370, 390, 420); performing a liveness test on the object using a deep convolutional neural network model-based liveness test model (450, 610), wherein the liveness test model: - uses the interest region (322, 324, 326, 328, 345, 370, 390, 420) as first input image information and the downscaled image as second input image information, both input to the liveness test model (450, 610), wherein the first input image information of the interest region (322, 324, 326, 328, 345, 370, 390, 420) and the second input information of the downscaled image are input into the liveness test model (450, 610) through, respectively, an input layer for the first input image information of the interest region (322, 324, 326, 328, 345, 370, 390, 420) 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 (450, 610); and indicating a result of the liveness test. receiving an input image; extracting an interest region (322, 324, 326, 328, 345, 370, 390, 420) of an object from the input image, wherein the interest region (322, 324, 326, 328, 345, 370, 390, 420) 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 (322, 324, 326, 328, 345, 370, 390, 420); performing a liveness test on the object using a deep convolutional neural network model-based liveness test model (450, 610), wherein the liveness test model: - uses the interest region (322, 324, 326, 328, 345, 370, 390, 420) as first input image information and the downscaled image as second input image information, both input to the liveness test model (450, 610), wherein the first input image information of the interest region (322, 324, 326, 328, 345, 370, 390, 420) and the second input information of the downscaled image are input into the liveness test model (450, 610) through, respectively, an input layer for the first input image information of the interest region (322, 324, 326, 328, 345, 370, 390, 420) 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 (450, 610); and - uses the interest region (322, 324, 326, 328, 345, 370, 390, 420) as first input image information and the downscaled image as second input image information, both input to the liveness test model (450, 610), wherein the first input image information of the interest region (322, 324, 326, 328, 345, 370, 390, 420) and the second input information of the downscaled image are input into the liveness test model (450, 610) through, respectively, an input layer for the first input image information of the interest region (322, 324, 326, 328, 345, 370, 390, 420) 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 (450, 610); and indicating a result of the liveness test.