Looking At Rectangular Haar Features Used in Cascade Face Detection

Basic Analysis of Haar Cascade Classifier for object detection Pi19404 July 16, 2013

Contents

Contents Basic Analysis of Haar Cascade Classier for object detection 0.1 Introduction . . . 0.2 Face Detection 0.3 Code . . . . . . . . References . . . . . . .

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Basic Analysis of Haar Cascade Classifier for object detection

Basic Analysis of Haar Cascade Classifier for object detection 0.1 Introduction In the article we will look at haar features which are produced by the output of the cascade classification process. analysing them will help us understand if our traning process if proceeding in the right direction and which are features that are considered for detection for haar cascade classifier.

0.2 Face Detection We will look at the Cascade Classifier file for face detection. The cascade files are taken from the opencv cascade data directory. The weak classifiers correspond to rectangular features that are considered important in detection process. For each of stages the haar feature window corresponding to the weak classifiers are plotted.Each rectangular feature is plotted with different color.There are about 21 cascade stages . here we have plotted 8 stages.The original image size is 20x20.The output has be resized for display. Looking at the cascades it is evident that the features captured by the haar cascades classifier are not at all intuitive especially the higher order cascades. This would suggest that it captures complex relationships in training image using these rectangular features and detects the objects using these .Observing the rectangular feature complex relationship captured does not seem obvious at all. The obvious drawback with haar cascade classifiers is result is heavily data dependent.Training process is requirement of relatively

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Basic Analysis of Haar Cascade Classifier for object detection high resolution data taken against simple backgrounds,under different lighting conditions and most importantly cropped well alligned and scaled data. Since preparation of such data is a tedious task ,we cannot easily adapt the technique for other objects .

0.3 Code The code for the testing and training utility can be found at https: //github.com/pi19404/m19404/tree/master/objdetect/haar1 you need to pass as argument the haar cascade tag name which come after the base opencv root node.

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Bibliography

Bibliography [1]

P. Viola and M. Jones.  Rapid object detection using a boosted cascade of simple features. In: Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on.

I511I518 vol.1. [2]

doi: 10.1109/CVPR.2001.990517.

Vol. 1. 2001,

Jianxin Wu et al.  Fast Asymmetric Learning for Cascade Face Detection. In: IEEE Trans. Pattern Anal. Mach. Intell.

30.3 (Mar. 2008), pp. 369382.

doi: 10.1109/TPAMI.2007.1181. 1109/TPAMI.2007.1181.

0162-8828.

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url: http://dx.doi.org/10.

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