Drowsiness Detection System

A machine-implemented method for automated detection of drowsiness, which includes:

• receiving onto processing hardware a series of images of an operator,
• processing the series of images of the operator on processing hardware by a set of trained convolution neural networks comprising:
• a face detection group into which at least one image of the series of images is received from the image sensor for detecting the face of the operator in the at least one image;
• a face tracking group which match the face in the at least one image in the series of images with the same face in a previous image in the series of images and which then track the motion of the same face in the further series of images;
• a facial landmark detection group which detects movement of a plurality of facial landmarks in the further series of images, the facial landmarks selected from the group of jawline, eyebrows, eyelids, nose, pupils and lips;
• a face pose detection group which determine the orientation of the face of the operator;
• and
• a drowsiness detection group, which includes any one or more of:
• a yawning and talking detection group, which is trained to determine whether an operator is talking, thereby to provide an indication of alertness, and whether an operator is yawning, thereby to provide an indication of drowsiness;
• a PERCLOS (percentage of eye closure over time) calculation, which determines the percentage of eye closure over a period of time, thereby to provide an indication of drowsiness if the PERCLOS exceeds a predefined value;
• a fixed gaze calculation, which determines when an operator's gaze time exceeds a predefined value, thereby to provide an indication of drowsiness;
• an erratic movement detection group, which is trained to determine when a vehicle's movement resembles that of a drowsy driver, thereby to provide an indication of drowsiness;
• ensembling the outputs received from any one or more of the yawning and talking detection group, the PERCLOS calculation, the a fixed gaze calculation, and the erratic movement detection group thereby to generate an alarm when the ensembled value exceed a predefined value for a drowsy operator;
• and
• generating an alarm when the level of drowsiness of the operator determined by the processing hardware exceeds a predefined value.

A machine-implemented method for automated detection of drowsiness, which includes

• receiving from an imaging device directed at the face of an operator a series of images of the face of the operator onto processing hardware,
• on the processor detecting facial landmarks of the operator from the series of images to determine the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator;
• on the processor detecting the facial pose of the operator from the series of images to determine the level of gaze fixation by the operator;
• on the processor calculating the level of drowsiness of the operator by ensembling the level of talking by the operator, the level of yawning of the operator, the PERCLOS of the operator and the level of gaze fixation by the operator;
• and
• generating an alarm when the calculated level of drowsiness of the operator exceeds a predefined value.