Face Detection - Techniques

Techniques

Many algorithms implement the face-detection task as a binary pattern-classification task. That is, the content of a given part of an image is transformed into features, after which a classifier trained on example faces decides whether that particular region of the image is a face, or not.

Often, a window-sliding technique is employed. That is, the classifier is used to classify the (usually square or rectangular) portions of an image, at all locations and scales, as either faces or non-faces (background pattern).

Images with a plain or a static background are easy to process. Remove the background and only the faces will be left, assuming the image only contains a frontal face.

Using skin color to find face segments is a vulnerable technique. The database may not contain all the skin colors possible. Lighting can also affect the results. Non-animate objects with the same color as skin can be picked up since the technique uses color segmentation. The advantages are the lack of restriction to orientation or size of faces and a good algorithm can handle complex backgrounds.

Faces are usually moving in real-time videos. Calculating the moving area will get the face segment. However, other objects in the video can also be moving and would affect the results. A specific type of motion on faces is blinking. Detecting a blinking pattern in an image sequence can detect the presence of a face. Eyes usually blink together and symmetrically positioned, which eliminates similar motions in the video. Each image is subtracted from the previous image. The difference image will show boundaries of moved pixels. If the eyes happen to be blinking, there will be a small boundary within the face.

A face model can contain the appearance, shape, and motion of faces. There are several shapes of faces. Some common ones are oval, rectangle, round, square, heart, and triangle. Motions include, but not limited to, blinking, raised eyebrows, flared nostrils, wrinkled forehead, and opened mouth. The face models will not be able to represent any person making any expression, but the technique does result in an acceptable degree of accuracy. The models are passed over the image to find faces, however this technique works better with face tracking. Once the face is detected, the model is laid over the face and the system is able to track face movements.

A method for human face detection from color videos or images is to combine various methods of detecting color, shape, and texture. First, use a skin color model to single out objects of that color. Next, use face models to eliminate false detections from the color models and to extract facial features such as eyes, nose, and mouth.