Adaptive accidental contact mitigation for touch data classification

1 . A system for classifying touch data, comprising:

a plurality of sensing elements; and

a processing system configured to:

receive touch data from a current user via the plurality of sensing elements;

apply a second set of classifier parameters to classify the touch data;

determine, based on usage data and the second set of classifier parameters, a first set of classifier parameters corresponding to the current user, wherein the usage data comprises the touch data from the current user; and

apply the first set of classifier parameters to classify subsequent touch data.

2 . The system of claim 1 , wherein the touch data comprises a touch image corresponding to a sensing region associated with the plurality of sensing elements.

3 . The system of claim 2 , wherein the processing system is further configured to:

generate one or more segmented touch images from the touch image, each segmented touch image corresponding to an active touch region in the touch image; and

classify the one or more segmented touch images based on the second set of classifier parameters.

4 . The system of claim 3 , wherein classifying the one or more segmented touch images based on the second set of classifier parameters further comprises:

extracting, for each segmented touch image of the one or more segmented touch images, one or more features associated with the respective segmented touch image; and

classifying the respective segmented touch image by applying the second set of classifier parameters to the one or more features associated with the respective segmented touch image of the one or more segmented touch images.

5 . The system of claim 1 , wherein the usage data further comprises touch data that has been previously stored in a database, wherein determining the first set of classifier parameters corresponding to the current user is further based on the touch data that has been previously stored in the database.

6 . The system of claim 5 , wherein the processing system is further configured to:

determine a first average value associated with the touch data from the current user;

determine a second average value associated with the touch data that has been previously stored in the database; and

update the first set of classifier parameters based on the first average value and the second average value.

7 . The system of claim 6 , wherein the first average value and the second average value are determined based on finger swipe characteristics.

8 . The system of claim 6 , wherein the first set of classifier parameters is dynamically updated with a predefined speed, wherein the predefined speed is associated with a tunable weight that corresponds to the first average value.

9 . The system of claim 8 , wherein the processing system is further configured to:

in response to detect a change of the tunable weight, dynamically update the first set of classifier parameters with an updated speed.

10 . A method for classifying touch data, comprising:

receiving, by a processing system, touch data from a current user;

applying, by the processing system, a second set of classifier parameters to classify the touch data;

determining, by the processing system, based on usage data and the second set of classifier parameters, a first set of classifier parameters corresponding to the current user, wherein the usage data comprises the touch data from the current user; and

applying, by the processing system, the first set of classifier parameters to classify subsequent touch data.

11 . The method of claim 10 , wherein the touch data comprises a touch image corresponding to a sensing region associated with a plurality of sensing elements.

12 . The method of claim 11 , further comprising:

generating one or more segmented touch images from the touch image, each segmented touch image corresponding to an active touch region in the touch image; and

classifying the one or more segmented touch images based on the second set of classifier parameters.

13 . The method of claim 12 , wherein classifying the one or more segmented touch images based on the second set of classifier parameters further comprises:

extracting, for each segmented touch image of the one or more segmented touch images, one or more features associated with the respective segmented touch image; and

classifying the respective segmented touch image by applying the second set of classifier parameters to the one or more features associated with the respective segmented touch image of the one or more segmented touch images.

14 . The method of claim 10 , wherein the usage data further comprises touch data that has been previously stored in a database, wherein determining the first set of classifier parameters corresponding to the current user is further based on the touch data that has been previously stored in the database.

15 . The method of claim 14 , further comprising:

determining a first average value associated with the touch data from the current user;

determining a second average value associated with the touch data that has been previously stored in the database; and

updating the first set of classifier parameters based on the first average value and the second average value.

16 . The method of claim 15 , wherein the first average value and the second average value are determined based on finger swipe characteristics.

17 . The method of claim 15 , wherein the first set of classifier parameters is dynamically updated with a predefined speed, wherein the predefined speed is associated with a tunable weight that corresponds to the first average value.

18 . The method of claim 17 , further comprising:

in response to detect a change of the tunable weight, dynamically updating the first set of classifier parameters with an updated speed.

19 . A non-transitory computer-readable medium, having computer-executable instructions stored thereon for classification of an input object using an input device, wherein the computer-executable instructions, when executed, facilitate performance of the following:

receiving touch data from a current user;

applying a second set of classifier parameters to classify the touch data;

determining, based on usage data and the second set of classifier parameters, a set of classifier parameters corresponding to the current user, wherein the usage data comprises the touch data from the current user; and

applying the first set of classifier parameters to classify subsequent touch data.

20 . The non-transitory computer-readable medium of claim 19 , wherein the usage data further comprises touch data that has been previously stored in a database, wherein determining the first set of classifier parameters corresponding to the current user is further based on the touch data that has been previously stored in the database.