Methods and systems for detecting user liveness
A method for detecting user liveness is provided that includes selecting first and second images from a sequence of images. The first and second images are captured under different illumination conditions. The method further includes locating feature calculation windows in corresponding positions on the first and second images. Each window includes a first area and a second area. Moreover, the method includes calculating, by a computing device, a feature value for each window position based on pixels, within the windows located at the position, from the first and second images. Furthermore, the method includes calculating a feature vector from the feature values, calculating a confidence score from the feature vector, and determining the sequence of images includes images of a live user when the confidence score is equal to or greater than the threshold score.
1. A method for detecting user liveness comprising the steps of:
locating feature calculation windows in different positions on an image of a biometric modality of a user, each window includes a first area and a second area;
calculating an average pixel value for each window and position from the pixels included in the first area, the pixels being of one color channel;
calculating an average pixel value for each window and position from the pixels included in the second area, the pixels from the second area being of a different color channel;
calculating a feature value for each window and position as the difference between the average pixel values;
calculating a feature vector from the feature values;
calculating a confidence score from the feature vector; and
determining the image was taken of a live user when the confidence score is equal to or greater than a threshold score.
2. A method for detecting user liveness in accordance with claim 1 , said locating step comprising:
selecting a window type and associated lighting condition, color channel, size and coordinates from a table; and
locating the selected window on the image in accordance with the coordinates.
3. A computing device for detecting user liveness comprising:
a processor; and
a memory configured to store data, said computing device being associated with a network and said memory being in communication with said processor and having instructions stored thereon which, when read and executed by said processor, cause said computing device to:
locate feature calculation windows in different positions on an image of a biometric modality of a user, each window includes a first area and a second area;
calculate an average pixel value for each window and position from the pixels included in the first area, the pixels being of one color channel;
calculate an average pixel value for each window and position from the pixels included in the second area, the pixels from the second area being of a different color channel;
calculate a feature value for each window and position as the difference between the average pixel values;
calculate a feature vector from the feature values;
calculate a confidence score from the feature vector; and
determine the image was taken of a live user when the confidence score is equal to or greater than a threshold score.
4. A computing device for detecting user liveness in accordance with claim 3 , wherein the instructions when read and executed by said processor, further cause said computing device to:
select a window type and associated lighting condition, color channel, size and coordinates from a table; and
locate the selected window on the image in accordance with the coordinates.
5. A non-transitory computer-readable recording medium included in a computing device having a computer program recorded thereon for detecting user liveness, the computer program being comprised of instructions, which when read and executed by the computing device, cause the computing device to:
locate feature calculation windows in different positions on an image of a biometric modality of a user, each window includes a first area and a second area;
calculate an average pixel value for each window and position from the pixels included in the first area, the pixels being of one color channel;
calculate an average pixel value for each window and position from the pixels included in the second area, the pixels from the second area being of a different color channel;
calculate a feature value for each window and position as the difference between the average pixel values;
calculate a feature vector from the feature values;
calculate a confidence score from the feature vector; and
determine the image was taken of a live user when the confidence score is equal to or greater than a threshold score.
6. A non-transitory computer-readable recording medium in accordance with claim 5 wherein the instructions when read and executed by said computing device, further cause said computing device to:
select a window type and associated lighting condition, color channel, size and coordinates from a table; and
locate the selected window on the image in accordance with the coordinates.