An estimator generation apparatus, comprising: a learning data obtainer configured to obtain a plurality of first learning datasets each including a combination of first face image data and first state information data, the first face image data including a face of a subject driving a vehicle, the first state information data representing a state of the subject during driving of the vehicle, wherein the first state information data includes the state of the subject including at least one selected from the group consisting of a drowsiness level indicating a level of drowsiness of the subject, a fatigue level indicating a level of fatigue of the subject, and a capacity level indicating a level of capacity of the subject for driving, and obtain a plurality of second learning datasets each including a combination of second face image data and first physiological data, the second face image data including the face of the subject, the first physiological data being obtained by measuring a physiological parameter about the subject with a sensor; and characterized by a learning processor configured to construct a first estimator by performing machine learning on the first estimator, the first estimator including an encoder and an estimation unit connected to the encoder to receive an output from the encoder, the first estimator being trained to output, from the estimation unit, in response to the encoder receiving an input of the first face image data included in a first learning dataset of the plurality of first learning datasets, an output value corresponding to the state of the subject estimated from the first face image data, said machine learning being performed on the first estimator comprising reducing an error between the output value and a value corresponding to the first state information data associated with the input first face image data in the first learning dataset, and construct a second estimator by performing machine learning on the second estimator, the second estimator including the encoder in the first estimator and a decoder connected to the encoder to receive an output from the encoder, the second estimator being trained to output, from the decoder, in response to the encoder receiving an input of the second face image data included in a second learning dataset of the plurality of second learning datasets, output data corresponding to a result of reconstruction of physiological data about the subject from the second face image data, said machine learning being performed on the second estimator comprising reducing an error between the output data and the first physiological data associated with the input second face image data in the second learning dataset. a learning data obtainer configured to obtain a plurality of first learning datasets each including a combination of first face image data and first state information data, the first face image data including a face of a subject driving a vehicle, the first state information data representing a state of the subject during driving of the vehicle, wherein the first state information data includes the state of the subject including at least one selected from the group consisting of a drowsiness level indicating a level of drowsiness of the subject, a fatigue level indicating a level of fatigue of the subject, and a capacity level indicating a level of capacity of the subject for driving, and obtain a plurality of second learning datasets each including a combination of second face image data and first physiological data, the second face image data including the face of the subject, the first physiological data being obtained by measuring a physiological parameter about the subject with a sensor; and characterized by a learning processor configured to construct a first estimator by performing machine learning on the first estimator, the first estimator including an encoder and an estimation unit connected to the encoder to receive an output from the encoder, the first estimator being trained to output, from the estimation unit, in response to the encoder receiving an input of the first face image data included in a first learning dataset of the plurality of first learning datasets, an output value corresponding to the state of the subject estimated from the first face image data, said machine learning being performed on the first estimator comprising reducing an error between the output value and a value corresponding to the first state information data associated with the input first face image data in the first learning dataset, and construct a second estimator by performing machine learning on the second estimator, the second estimator including the encoder in the first estimator and a decoder connected to the encoder to receive an output from the encoder, the second estimator being trained to output, from the decoder, in response to the encoder receiving an input of the second face image data included in a second learning dataset of the plurality of second learning datasets, output data corresponding to a result of reconstruction of physiological data about the subject from the second face image data, said machine learning being performed on the second estimator comprising reducing an error between the output data and the first physiological data associated with the input second face image data in the second learning dataset.