Fastandreliablealgorithmsforestimatingtheheadpose are essential for many applications and higher-level face analysis tasks. We address the problem of head pose estimation from depth data, which can be captured using the evermore affordable3D sensingtechnologiesavailabletoday. To achieve robustness, we formulate pose estimation asaregressionproblem. Whiledetectingspecificfaceparts like the nose is sensitive to occlusions, learning the regression on rather generic surface patches requires enormous amount of training data in order to achieve accurate estimates. We proposeto use randomregressionforests for the taskathand,giventheircapabilitytohandlelargetraining datasets. Moreover, we synthesize a great amountof annotated training data using a statistical model of the human face. In our experiments, we show that our approach can handlereal datapresentinglargepose changes,partialocclusions, and facial expressions, even though it is trained only on synthetic neutral face data. We have thoroughly evaluated our system on a publicly available database on whichweachievestate-of-the-artperformancewithouthavingto resort tothegraphicscard. 1
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