Affectiva, Inc.

Survi Kyal

Rana El Kaliouby

Panu James Turcot

Abdelrahman N Mahmoud

Seyedmohammad Mavadati

Ajjen Das Joshi

Taniya Mishra

Sandipan Banerjee

Machine learning is performed using synthetic data for neural network training using vectors. Facial images are obtained for a neural network training dataset. Facial elements from the facial images are encoded into vector representations of the facial elements. A generative adversarial network (GAN) generator is trained to provide one or more synthetic vectors based on the one or more vector representations, wherein the one or more synthetic vectors enable avoidance of discriminator detection in the GAN. The training a GAN further comprises determining a generator accuracy using the discriminator. The generator accuracy can enable a classifier, where the classifier comprises a multi-layer perceptron. Additional synthetic vectors are generated in the GAN, wherein the additional synthetic vectors avoid discriminator detection. A machine learning neural network is trained using the additional synthetic vectors. The training a machine learning neural network further includes using the one or more synthetic vectors.

Synthetic data for neural network training using vectors

Disclosed techniques include in-vehicle drowsiness analysis using blink-rate. Video of an individual is obtained within a vehicle using an image capture device. The video is analyzed using one or more processors to detect a blink event based on a classifier for a blink that was determined. Using temporal analysis, the blink event is determined by identifying that eyes of the individual are closed for a frame in the video. Using the blink event and one or more other blink events, blink-rate information is determined using the one or more processors. Based on the blink-rate information, a drowsiness metric is calculated using the one or more processors. The vehicle is manipulated based on the drowsiness metric. A blink duration of the individual for the blink event is evaluated. The blink-rate information is compensated. The compensating is based on demographic information for the individual.

In-vehicle drowsiness analysis using blink rate

Drowsiness mental state analysis is performed using blink rate. Video is obtained of an individual or group. The individual or group can be within a vehicle. The video is analyzed to detect a blink event based on a classifier, where the blink event is determined by identifying that eyes are closed for a frame in the video. A blink duration is evaluated for the blink event. Blink-rate information is determined using the blink event and one or more other blink events. The evaluating can include evaluating blinking for a group of people. The blink-rate information is compensated to determine drowsiness, based on the temporal distribution mapping of the blink-rate information. Mental states of the individual are inferred for the blink event based on the blink event, the blink duration of the individual, and the blink-rate information that was compensated. The compensating is biased based on demographic information of the individual.

Drowsiness mental state analysis using blink rate

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Chestnut Hill, MA, United States of America

Survi Kyal