A processor-implemented object recognition method, the method comprising the steps of: acquiring an object image including a plurality of consecutive frames, with a target image and an input image being select frames of the object image; selecting the input image; verifying the input image using an object model, comprising a deep neural network that includes partial layers providing intermediate data and an output layer providing output data, wherein the verifying of the input image comprises: object recognition of the input image using the output data; characterized by storing a reference intermediate data; and liveness verification of the input image, and in response to a failure of the liveness verification of the input image after a success of the object recognition of the input image: selecting the target image, wherein the target image is subsequent to the input image; verifying the target image using the object model and the reference intermediate data previously extracted by a partial layer of the object model as used in object recognition of the input image, wherein the verifying of the target image comprises: calculating target intermediate data provided by the partial layer of the object model; determining whether the target intermediate data matches the reference intermediate data; and calculating target output data for the target image as a result of completion of object recognition of the target image that further uses the entire object model when the target intermediate data does not match the reference intermediate data, and performing liveness verification of the target image, in response to the target image being verified in the verifying of the target image when the target intermediate data matches the reference intermediate data or when the object recognition using the entire object model is successful. acquiring an object image including a plurality of consecutive frames, with a target image and an input image being select frames of the object image; selecting the input image; verifying the input image using an object model, comprising a deep neural network that includes partial layers providing intermediate data and an output layer providing output data, wherein the verifying of the input image comprises: object recognition of the input image using the output data; characterized by storing a reference intermediate data; and liveness verification of the input image, object recognition of the input image using the output data; characterized by storing a reference intermediate data; and liveness verification of the input image, and in response to a failure of the liveness verification of the input image after a success of the object recognition of the input image: selecting the target image, wherein the target image is subsequent to the input image; verifying the target image using the object model and the reference intermediate data previously extracted by a partial layer of the object model as used in object recognition of the input image, wherein the verifying of the target image comprises: calculating target intermediate data provided by the partial layer of the object model; determining whether the target intermediate data matches the reference intermediate data; and calculating target output data for the target image as a result of completion of object recognition of the target image that further uses the entire object model when the target intermediate data does not match the reference intermediate data, and performing liveness verification of the target image, in response to the target image being verified in the verifying of the target image when the target intermediate data matches the reference intermediate data or when the object recognition using the entire object model is successful. selecting the target image, wherein the target image is subsequent to the input image; verifying the target image using the object model and the reference intermediate data previously extracted by a partial layer of the object model as used in object recognition of the input image, wherein the verifying of the target image comprises: calculating target intermediate data provided by the partial layer of the object model; determining whether the target intermediate data matches the reference intermediate data; and calculating target output data for the target image as a result of completion of object recognition of the target image that further uses the entire object model when the target intermediate data does not match the reference intermediate data, and calculating target intermediate data provided by the partial layer of the object model; determining whether the target intermediate data matches the reference intermediate data; and calculating target output data for the target image as a result of completion of object recognition of the target image that further uses the entire object model when the target intermediate data does not match the reference intermediate data, and performing liveness verification of the target image, in response to the target image being verified in the verifying of the target image when the target intermediate data matches the reference intermediate data or when the object recognition using the entire object model is successful.