14. A computer-implemented method for machine access control, comprising: capturing, by a camera, an input image of a subject purported to be a person associated with operating a particular workplace machine; storing, in a memory, a deep learning model configured to perform joint multi-task learning for a pair of tasks including a liveness detection task and a face recognition task; and applying, by a processor, the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized to use the particular workplace machine and a liveness of the subject, and wherein the liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task.
14. A computer-implemented method for machine access control, comprising: capturing, by a camera, an input image of a subject purported to be a person associated with operating a particular workplace machine; storing, in a memory, a deep learning model configured to perform joint multi-task learning for a pair of tasks including a liveness detection task and a face recognition task; and applying, by a processor, the deep learning model to the input image to recognize an identity of the subject in the input image regarding being authorized to use the particular workplace machine and a liveness of the subject, and wherein the liveness detection task is configured to evaluate a plurality of different distractor modalities corresponding to different physical spoofing materials to prevent face spoofing for the face recognition task. 17. The computer-implemented method of claim 14 , further comprising generating an alarm indicating an unauthorized attempted use of the workplace machine, responsive to results of the liveness detection task and the face recognition task.