A method for identifying a gesture from one of a plurality of dynamic gestures, each dynamic gesture comprising a distinct movement made by a user over a period of time within a field of view of an image acquisition device, said method comprising: iteratively: acquiring a current image (200, X(t)) from said image acquisition device at a given time; passing at least a portion (204, 208) of said current image through a current instance of a multi-layer (20,30,40) classifier network, a first part of the classifier network comprising a first plurality of layers configured to compress said portion of said current image into a low spatial resolution feature map, and a second part of the classifier network comprising a plurality of classification heads (30-1...30-N), each instance of classification head comprising a plurality of layers configured to produce a respective classification from said feature map, at least one of said classification heads being configured to: receive a hidden layer output (fh(t-1) from a first corresponding intermediate layer (LSTM-A) of a previous instance of said classification head; provide a hidden layer output (fh(t)) to said first corresponding intermediate layer of a successive instance of said classification head; provide a hidden layer output (bh(t-1)) to a second corresponding intermediate layer (LSTM-B) of said previous instance of said classification head; and receive a hidden layer output (bh(t)) from said second corresponding intermediate layer of said successive instance of said classification head; and combining respective classifications from said classification heads and providing a final layer output indicating a probability that a gesture from said plurality of dynamic gestures is being made by a user during the time of acquiring said image. acquiring a current image (200, X(t)) from said image acquisition device at a given time; passing at least a portion (204, 208) of said current image through a current instance of a multi-layer (20,30,40) classifier network, a first part of the classifier network comprising a first plurality of layers configured to compress said portion of said current image into a low spatial resolution feature map, and a second part of the classifier network comprising a plurality of classification heads (30-1...30-N), each instance of classification head comprising a plurality of layers configured to produce a respective classification from said feature map, at least one of said classification heads being configured to: receive a hidden layer output (fh(t-1) from a first corresponding intermediate layer (LSTM-A) of a previous instance of said classification head; provide a hidden layer output (fh(t)) to said first corresponding intermediate layer of a successive instance of said classification head; provide a hidden layer output (bh(t-1)) to a second corresponding intermediate layer (LSTM-B) of said previous instance of said classification head; and receive a hidden layer output (bh(t)) from said second corresponding intermediate layer of said successive instance of said classification head; and combining respective classifications from said classification heads and providing a final layer output indicating a probability that a gesture from said plurality of dynamic gestures is being made by a user during the time of acquiring said image.