An image processing method for deriving a skeletal hand model , comprising: receiving , from an infrared, IR, camera, a signal encoding an IR image including a plurality of IR pixels , each IR pixel specifying one or more IR parameters of that IR pixel ; identifying , in the IR image , IR-skin pixels that image a human hand; for each IR-skin pixel , estimating a depth of a human hand portion imaged by that IR-skin pixel based on the IR parameters of that IR-skin pixel , wherein, for each IR-skin pixel , the depth of the human hand portion imaged by that IR-skin pixel is estimated via a previously-trained machine learning algorithm trained with two-dimensional, 2D, IR images and three-dimensional, 3D, depth maps annotated with ground truths; and deriving a skeletal hand model including a plurality of hand joints , each hand joint defined with three independent position coordinates inferred from the estimated depths of each human hand portion; wherein deriving the skeletal hand model includes: assembling a depth map including a depth for each IR-skin pixel ; identifying , based on one or both of the IR-skin pixels and the depth map , anatomical features of the human hand; and estimating , using the anatomical features and the depth map , a position of a joint of the human hand; wherein deriving the skeletal hand model further includes using at least one randomly perturbed hand joint position estimated from a previous-frame depth map to minimize an error function defined as a function of a spatial correspondence between the skeletal hand model and the IR-skin pixels . receiving , from an infrared, IR, camera, a signal encoding an IR image including a plurality of IR pixels , each IR pixel specifying one or more IR parameters of that IR pixel ; identifying , in the IR image , IR-skin pixels that image a human hand; for each IR-skin pixel , estimating a depth of a human hand portion imaged by that IR-skin pixel based on the IR parameters of that IR-skin pixel , wherein, for each IR-skin pixel , the depth of the human hand portion imaged by that IR-skin pixel is estimated via a previously-trained machine learning algorithm trained with two-dimensional, 2D, IR images and three-dimensional, 3D, depth maps annotated with ground truths; and deriving a skeletal hand model including a plurality of hand joints , each hand joint defined with three independent position coordinates inferred from the estimated depths of each human hand portion; wherein deriving the skeletal hand model includes: assembling a depth map including a depth for each IR-skin pixel ; identifying , based on one or both of the IR-skin pixels and the depth map , anatomical features of the human hand; and estimating , using the anatomical features and the depth map , a position of a joint of the human hand; wherein deriving the skeletal hand model further includes using at least one randomly perturbed hand joint position estimated from a previous-frame depth map to minimize an error function defined as a function of a spatial correspondence between the skeletal hand model and the IR-skin pixels . assembling a depth map including a depth for each IR-skin pixel ; identifying , based on one or both of the IR-skin pixels and the depth map , anatomical features of the human hand; and estimating , using the anatomical features and the depth map , a position of a joint of the human hand; wherein deriving the skeletal hand model further includes using at least one randomly perturbed hand joint position estimated from a previous-frame depth map to minimize an error function defined as a function of a spatial correspondence between the skeletal hand model and the IR-skin pixels . <EOS>