OpenCV is an open source computer vision and machine learning software library. It is a BSD-licence product thus free for both business and academic purposes.The Library provides more than 2500 algorithms that include machine learning tools for classification and clustering, image processing and vision algorithm, basic algorithms and drawing functions, GUI and I/O functions for images and videos. Some applications of these algorithms include face detection, object recognition, extracting 3D models, image processing, camera calibration, motion analysis etc.
OpenCV is written natively in C/C++. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS, and Android. OpenCV was designed for computational efficiency and targeted for real-time applications. Written in optimized C/C++, the library can take advantage of multi-core processing.
Face Detection Face detection has gained a lot of attention due to its real-time applications. A lot of research has been done and still going on for improved and fast implementation of the face detection algorithm. Why is face detection difficult for a machine? Face detection is not as easy as it seems due to lots of variations of image appearance, such as pose variation (front, non-front), occlusion, image orientation, illumination changes and facial expression.
OpenCV contains many pre-trained classifiers for face, eyes, smile etc. The XML files of pre-trained classifiers are stored in opencv/data/. For face detection specifically, there are two pre-trained classifiers:
- Haar Cascade Classifier
- LBP Cascade Classifier We will explore both face detectors in this tutorial.
Haar Cascade Classifier It is a machine learning based approach where a cascade function is trained from a lot of positive (images with face) and negative images (images without face). The algorithm is proposed by Paul Viola and Michael Jones.
The algorithm has four stages:
- Haar Feature Selection: Haar features are calculated in the subsections of the input image. The difference between the sum of pixel intensities of adjacent rectangular regions is calculated to differentiate the subsections of the image. A large number of haar-like features are required for getting facial features.
- Creating an Integral Image: Too much computation will be done when operations are performed on all pixels, so an integral image is used that reduce the computation to only four pixels. This makes the algorithm quite fast.
- Adaboost: All the computed features are not relevant for the classification purpose. Adaboost is used to classify the relevant features.
- Cascading Classifiers: Now we can use the relevant features to classify a face from a non-face but algorithm provides another improvement using the concept of cascades of classifiers. Every region of the image is not a facial region so it is not useful to apply all the features on all the regions of the image. Instead of using all the features at a time, group the features into different stages of the classifier.Apply each stage one-by-one to find a facial region. If on any stage the classifier fails, that region will be discarded from further iterations. Only the facial region will pass all the stages of the classifier.