Hand gesture recognition using Adaboost with SIFT

hand posture detection using sift

hand gesture segmentation

In a early post openCV was used to segment natural hand gestures from complicated backgrounds in real environments, as the picture above showed (see the original post hand gesture detection and recognition using openCV).

hand posture detection using sift

The picture above is a screenshot from a recent paper, about using Ataboost with SIFT (scale invariant feature transform), to detect natural hand gestures. The SIFT is used in the paper to reduce the background noise in the training stage, and so experimental results demonstrated in the paper show the approach performs with high accuracy.

So, the the adaboost learning algorithm is used after the SIFT background noise reducing. Relevant functions corresponding to these two algorithms have not been checked in openCV, to see if there is any working functions to realise such algorithms. However, more details about the use of Adaboost and SIFT, the paper hand posture recognition using Adaboost with sift for human robot interaction is reachable in the download page.

Author: Andol Li

A HCI researcher, a digital media lecturer, an information product designer, and a python/php/java coder.

5 Comments On “ Hand gesture recognition using Adaboost with SIFT”

  1. hello sir , I had detected a frame which contains a hand image from live cam..How to detect the frame that is repeated for about 2 seconds(say for about 10 consecutive frames)…

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