Regression algorithmImage analysisHead pose estimation is a very in-depth topic in the context of biometric recognition, especially in video surveillance, because the rotation of the head can affect the recognition of some features of the face. Being able to recognize in advance the pose of the ...
Different from most pose estimation algorithms that return an explicit pose direction, the proposed algorithm returns a probability density function over the range [−90 degrees, +90 degrees]−90°,+90°. The hierarchical graph model contains three levels. The lower two levels capture face ...
"We replace the suppression algorithm with a blending strategy that estimates the regression parameters of a bounding box as a weighted mean between the overlapping predictions." The original MediaPipe code assigns the score of the most confident detection to the weighted detection, but we take the...
global channel-wise facial feature information by explicitly modeling the interdependencies between the feature channels. Finally, a multiregression loss function is presented to improve the accuracy and stability for a full-range view of the head pose estimation. In addition, our method is compact ...
Supervised Descent Method Apply to Face Alignment, and Head Pose Estimation with Linear Regression. It is cross-platfrom, easily compile in windows, ubuntu, even in Android & iOS. - tonyzzzzz/sdm
On the other hand, deep neural networks provide good performances on head pose estimation (HPE) from a single image, thus promising for medical CROM measurement. We propose to use CNN networks to extract pyramidal or multi-level image features, which are passed to cross-level attention modules ...
the 2-P central difference algorithm results in gain suppression nearfnwithout exhibiting phase shifts as opposed to other non-symmetric techniques wherein signal delay is not constant. Phase offsets due to anti-aliasing filters were removed by performing Zero-Phase filtering42. To further reduce ...
We selected Bayesian regularization backpropagation60 as our training algorithm after comparing its performance with that of two others: Levenberg-Marquardt61 and scaled conjugate gradient62. The final architecture of the proposed method (3D DCNN for feature extraction and ANN for regression) is shown ...
al. [22] propose a generalized adaptive view-based appearance model (extension of the AVAM algorithm of [23]) that estimates the head pose for a specific image region. The final pose is inferred by merging the results of (1) a referential frame, (2) tracking between current and previous ...
This is explained by the orientation of the model, with the face normal matching the X-axis. As the face detection algorithm is very dependent on the angle of view, the geometry of the images results in a higher error in the X-direction. This fact has to be taken into account for ...