1. A machine access control system, comprising: a camera configured to capture an input image of a subject purported to be a person associated with operating a particular workplace machine; a memory storing 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 a processor configured to apply 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.
1. A machine access control system, comprising: a camera configured to capture an input image of a subject purported to be a person associated with operating a particular workplace machine; a memory storing 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 a processor configured to apply 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. 2. The access control system of claim 1 , wherein the processor is further configured to control a powered status of the workplace machine, responsive to results of the liveness detection task and the face recognition task, the powered status selectively being controlled to one of powered and unpowered.