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Yep wrong assumption. Let me add some clarity through some publications.

• The cat FACE detection and face detection are with this technique. This is because the face on its own is kind of rigid, so it works well. This is of course applicable to all the models found in https://github.com/opencv/opencv/tree/master/data
• You can see that we also have fullbody models there, but they basically perform weakly. Not really due to the rigid content, but due to the technique being outperformed by many techniques out there.
• Another rigid approach is the HOG+SVM approach described by dallal and triggs.
• If you really want rigid models, which can perform good, go for ICF/ACF for example of Piotr Dollar.
• Non rigid models are part based models, like the latent SVM models of Felzenswalb.

But if you want it even more exotic, go for the latest trend which is neural net processing. Techniques like CNN, RCNN, DNN, ... are top notch object detectors in their field!

Yep wrong assumption. Let me add some clarity through some publications.

• The cat FACE detection and face detection are with this technique. This is because the face on its own is kind of rigid, so it works well. This is of course applicable to all the models found in https://github.com/opencv/opencv/tree/master/data
• You can see that we also have fullbody models there, but they basically perform weakly. Not really due to the rigid content, but due to the technique being outperformed by many techniques out there.
• Another rigid approach is the HOG+SVM approach described by dallal and triggs.
• If you really want rigid models, which can perform good, go for ICF/ACF for example of Piotr Dollar.
• Non rigid models are part based models, like the latent SVM models of Felzenswalb.

But if you want it even more exotic, go for the latest trend which is neural net processing. Techniques like CNN, RCNN, DNN, ... are top notch object detectors in their field!