Haar-like的優勢在於能更好的描述明暗變化，因此用於檢測正面的人臉
HoG的優勢在於能更好的描述形狀，在行人識別方面有很好的效果
LBP比haar快很多倍，但是提取的準確率會低（10-20% 取決於訓練物件）如果是嵌入式或者移動端的開發，推薦使用LBP。

這也解釋了為什麼haar應用於人的正面檢測要明顯好於應用於側臉檢測：正臉由於鼻子等凸起的存在，使得臉上的光影變化十分明顯。而側臉側臉最重要的特徵是形狀和輪廓。 所以用HoG描述符檢測側臉更加有效。

參考原文：

https://www.quora.com/Why-are-HOG-features-more-accurate-than-Haar-features-in-pedestrian-detection 


It's important to look at the most prominent feature of pedestrians. There can be more than one prominent feature but the defining feature of a typical pedestrian is the outline, the legs and head shape. Hence the detection method that best captures or describes the pedestrian outline will ultimately solve the pedestrian detection problem more accurately. HoG features are capable of capturing the pedestrian or object outline/shape better than Haar features. On the other hand, simple Haar-like features can detect regions brighter or darker than their immediate surrounding region better than HoG features. In short HoG features can describe shape better than Haar features and Haar features can  describe shading better than HoG features.That is also why Haar features are good at detecting frontal faces and not so good for detecting profile faces. This is because the frontal face has features such as the nose bridge which is brighter than the surrounding face region. But the profile face most prominent feature is it's outline or shape, hence HoG would perform better for profile faces. HoG and Haar-like features are complementary features, hence combining them might even result in better performance. HoG features are good at describing object shape hence good for pedestrian detection. Whereas Haar features are good at describing object shading hence good for frontal face detection.