A gesture recognition system for remote control of a device, comprising: (a) a sensor for capturing video data of a user's hand at a location near the device, the captured video data comprising one or more hand images; (b) a processor for processing the captured video data of the hand; and (c) programming executable on said processor for carrying out steps comprising: segmenting each hand image in the captured video data; extracting one or more parameters of the segmented hand image; tracking the one or more parameters in the hand image; classifying a hand gesture as "open" or "closed" based on the one or more parameters; and operating the device based on recognition of said hand gesture and tracking a position of said one or more parameters, wherein segmenting each hand image comprises: determining hand position and size based on skin color and defining a hand bounding box; separating a foreground area associated with said hand from a background area surrounding said foreground area by using the hand bounding box; training a 3-component Gaussian mixture model for the image pixels of the background area and foreground area based on skin color; and classifying each pixel of the image pixels based on the ratio of the foreground area to the background area of the previous image, the probability of the pixel belonging to the foreground area and the probability of the pixel belonging to the background area, wherein each of said probabilities is computed using the 3-component Gaussian mixture model. (a) a sensor for capturing video data of a user's hand at a location near the device, the captured video data comprising one or more hand images; (b) a processor for processing the captured video data of the hand; and (c) programming executable on said processor for carrying out steps comprising: segmenting each hand image in the captured video data; extracting one or more parameters of the segmented hand image; tracking the one or more parameters in the hand image; classifying a hand gesture as "open" or "closed" based on the one or more parameters; and operating the device based on recognition of said hand gesture and tracking a position of said one or more parameters, wherein segmenting each hand image comprises: determining hand position and size based on skin color and defining a hand bounding box; separating a foreground area associated with said hand from a background area surrounding said foreground area by using the hand bounding box; training a 3-component Gaussian mixture model for the image pixels of the background area and foreground area based on skin color; and classifying each pixel of the image pixels based on the ratio of the foreground area to the background area of the previous image, the probability of the pixel belonging to the foreground area and the probability of the pixel belonging to the background area, wherein each of said probabilities is computed using the 3-component Gaussian mixture model. segmenting each hand image in the captured video data; extracting one or more parameters of the segmented hand image; tracking the one or more parameters in the hand image; classifying a hand gesture as "open" or "closed" based on the one or more parameters; and operating the device based on recognition of said hand gesture and tracking a position of said one or more parameters, wherein segmenting each hand image comprises: determining hand position and size based on skin color and defining a hand bounding box; separating a foreground area associated with said hand from a background area surrounding said foreground area by using the hand bounding box; training a 3-component Gaussian mixture model for the image pixels of the background area and foreground area based on skin color; and classifying each pixel of the image pixels based on the ratio of the foreground area to the background area of the previous image, the probability of the pixel belonging to the foreground area and the probability of the pixel belonging to the background area, wherein each of said probabilities is computed using the 3-component Gaussian mixture model. determining hand position and size based on skin color and defining a hand bounding box; separating a foreground area associated with said hand from a background area surrounding said foreground area by using the hand bounding box; training a 3-component Gaussian mixture model for the image pixels of the background area and foreground area based on skin color; and classifying each pixel of the image pixels based on the ratio of the foreground area to the background area of the previous image, the probability of the pixel belonging to the foreground area and the probability of the pixel belonging to the background area, wherein each of said probabilities is computed using the 3-component Gaussian mixture model.