Cascade Classifier 👤 Face Detection

Computer vision is how computers automate tasks that mimic human response to visual information. Image features such as points, edges, or patterns are used to identify an object in an image. A cascade classifier uses these visual cues as features to determine if an object is in the image, such as a face.

Image Features
A feature is a piece of information about the content of an image typically about whether a certain region of the image has certain properties. Features can be identified by color, shape, or size of the object.

Features may also be characteristics of the general neighborhood or specific to a small region. Changes from frame to frame in image sequences may also be important features to detect a moving object relative to the surroundings. More broadly a feature is any piece of information which is relevant for solving the computational task related to a certain application. This is the same sense as a feature in machine learning and pattern recognition generally, though image processing has a very sophisticated collection of features. The feature concept is very general and the choice of features in a particular computer vision system may be highly dependent on the specific problem at hand.

Harris Corner
Local Binary Pattern (LBP)
SIFT (Scale Invariant Feature Transform)
Histogram of Oriented Gradient (HoG)
SURF (Speeded Up Robust Feature) inspired by SIFT
FAST (Features from Accelerated Segment Test)
SUSAN (Smallest Univalue Segment Assimilating Nucleus)
BASIS (BAsis Sparse-coding Inspired Similarity)
SYBA (SYthetic BAsis Functions)

Haar-like Features
A Haar-like feature considers adjacent rectangular regions at a specific location in a detection window, sums up the pixel intensities in each region and calculates the difference between these sums. This modified feature set is called 2-rectangle feature. Viola and Jones also defined 3-rectangle features and 4-rectangle features. The detection approach was proposed by Viola and Jones in 2001: Rapid Object Detection using a Boosted Cascade of Simple Features.

The values indicate certain characteristics of a particular area of the image. Each feature type can indicate the existence (or absence) of certain characteristics in the image, such as edges or changes in texture. For example, a 2-rectangle feature can indicate where the border lies between a dark region and a light region.

Haar Cascade Classifier
Cascading is a particular case of ensemble learning based on the concatenation of several classifiers, using all information collected from the output from a given classifier as additional information for the next classifier in the cascade. Unlike voting or stacking ensembles (multiexpert systems), cascading is a multistage method.

Cascading classifiers are trained with positive sample views of a particular object and arbitrary negative images of the same size. After the classifier is trained it can be applied to a region of an image and detect the object in question. To search for the object in the entire frame, the search window can be moved across the image and check every location for the classifier. This process is most commonly used in image processing for object detection and tracking, primarily facial detection and recognition.

Cascades are usually done through cost-aware AdaBoost. The sensitivity threshold (0.8 in our example) can be adjusted so that there is close to 100% true positives and some false positives. The procedure can then be started again for stage 2, until the desired accuracy/computation time is reached.

After the initial algorithm, it was understood that training the cascade as a whole can be optimized, to achieve a desired true detection rate with minimal complexity. Examples of such algorithms are RCBoost, ECBoost or RCECBoost. In their most basic versions, they can be understood as choosing, at each step, between adding a stage or adding a weak learner to a previous stage, whichever is less costly, until the desired accuracy has been reached. Every stage of the classifier cannot have a detection rate (sensitivity) below the desired rate, so this is a constrained optimization problem. To be precise, the total sensitivity is the product of stage sensitivities.

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