Face Detection Research PDF

Face Detection The best implementation process of a face detection system should include: - **Image Processing of face images,** **Feature Extraction techniques** **Neural Networks techniques** That means learning about Image Processing, Face Detection, Neural Networks, Image Feature Extraction...

Face Detection The best implementation process of a face detection system should include: - **Image Processing of face images,** **Feature Extraction techniques** **Neural Networks techniques** That means learning about Image Processing, Face Detection, Neural Networks, Image Feature Extraction and Gabor Feature. Applying previous steps could be based on MATLAB, so it is assumed that you have MATLAB and both image processing and neural network toolboxes installed. Detecting faces or in general, objects, inside gray-level images is still a hot topic of research. Face detection is the first and foremost step in any automated face recognition system. Its reliability greatly affects the performance and usability of the whole system. Given a single image or a video frame, an ideal face detector should have the ability to locate all the present faces inside that image, regardless of their position, facial gestures, variations in scale and orientation. Furthermore, it should be robust against variation in illumination, skin color or background... Several clues may facilitate the detection process. Skin color (for detecting faces in colorful images and videos) is one that often can be used. Motion (for detecting faces in video) is another well-known clue that can be estimated by analyzing several video frames in a row. But the hardest kind of detection is face detector in grey-level still images, in which there is no cue of any type such as color or motion. - **Step One: Feature Extraction Function **: You need a function that can transform a small patch of image into a vector. If you only reshape the 2D patch into a 1D vector, it is still a function and correct. But in practice it contains several stages. This function is the feature extraction function and should extract features in a wise manner, followed by a normalization. In Face Detection System for MATLAB, Gabor features are extracted from the patch. - **Step Two: Generating Data for Training** - **Step Three: Train The Classifier: **: Every detection system needs a classifier that looks at your vector and decides if it is the deal or not. In case of face detection, the classifier looks for faces. The main issues are to choose your classifier and set the parameters in a way that you get reasonable results. Face Detection System for MATLAB uses Neural Network as its classifier. Everything regarding how to generate the network and train it is discussed inside the guide in detail. - **Step Four: Scan a Picture **: Then an image is scanned at all possible locations \[and scales\] by a sub-window (patch). Each patch is fed to the feature extraction function and the output vector goes to the classifier. There are ways to pre-select possible locations and how to pin point the location of the faces. - **Imgage Processing In MATLAB** - **Artificial Neural Networks **: Perceptron Neuron, Single Layer Perceptron, Multi-Layer Feed-Forward Network, Backpropagation Training, Creating Networks, Inputs and Outputs, Initialization, Testing and Simulation, Training Set Generation - **Feature Extraction **: (Gray-Level Features -- Histogram Equalization -- Illumination Effects -- Normalization) - **Gabor Features and Gabor Feature Extraction **: Concept, Code and Fast Gabor Transform - **Other Elements of a Face Detection System **: Pre-Selection, Search Algorithm and Post-Processing