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Detection on Faces. Levente Saj ó. Detecting Face. Knowledge-Based Methods A face often appears in an image with two eyes that are symmetric to each other, a nose and a mouth Problem: it is difficult to translate human knowledge into well defined rules Feature-Invariant Approaches
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Detection on Faces Levente Sajó
Detecting Face • Knowledge-Based Methods • A face often appears in an image with two eyes that are symmetric to each other, a nose and a mouth • Problem: it is difficult to translate human knowledge into well defined rules • Feature-Invariant Approaches • Facial Features • Using edge detectors, facial features such as eyes, nose, mouth, and hair line are extracted • A statistical model is built to describe their relationships • Problem: lighting, shadows • Skin Color
Detecting Face • Template Matching Methods • Predefined templates • correlation values with the standard patterns are computedeg.: for the face contour, eyes, nose and mouthindependently • it can not effectively deal with variation in scale, pose and shape • Deformable Templates – Shape Models • How to represent the template? • How to model deformations? • Efficient matching algorithms
Detecting Face Appearance-Based Methods • Eigenfaces – PCA • Neural Networks • SVM • AdaBOOST (by Viola and Jones, 2001) • Quite accurate • Fastest – 10-15-times faster then the others • An extended version is implemented in opencv (by Lienhart, 2002) • This method is used in Google Portrait, too
Boosted Cascade Detector • Consists 3 parts • Encoding the image data known as “integral image” • Running the boosting algorithm known as AdaBoost, to select the appropriate feature • a cascade of templates that allows simple feature sets to quickly discard most of the uninteresting parts of the image
Integral Image • An integral image is constructed by replacing each image pixel value i(x,y) with a value that corresponds to the pixel sum, above and to the left of the pixel
Feature Classifiers • Each classifier consists of a set of adjacent positive and negative regions • Different feature types: • If template is 24x24, then it produces 30000 different feature classifiers • Each individual feature classifier attempts to discriminate between faces/non-faces by summing the difference between adjacent regions
Boosting Algorithm • Boosting algorithms aim to combine many “weak classifiers” into one effective “strong classifier”. • AdaBoost achieves this by selecting the more promising classifiers.
Cascaded Detectors • A series of classifiers are applied to every sub-window. • The initial classifier eliminates a large number of negative examples with very little processing. • Subsequent layers eliminate additional negatives
BCD Experiments • Detecting whole face • Detecting facial features • The whole face template is more robust than any of the facial feature templates
Facial Expression Recognition • Recognizing the basic emotional expressions from video • neutral, anger, joy, sadness, surprise, disgust, fear • Steps: • Localizing the face • Preprocessing !! • Classification (with SVM, AdaBoost)
Preprocessing 1 • Gábor Transformation (Bartlett, 2003) • 48x48 face images were used • Normalized so, that the approximate distance between center of eyes to be 24 pixel • The images were converted into a Gabor magnitude representation, using a bank of Gabor filters at 8 orientations and 5 spatial frequencies
Preprocessing 2 • Feature Extraction (Michel, El Kaliouby, 2003) • In earlier work, using an SVM classifier on displacements of manually defined facial landmarks in image sequences yielded high classification accuracy • Uses a face template to initially locate the position of the 22 facial features • Their position is tracked over subsequent frames • A vector of feature displacements is calculated by taking the Euclidean distance between feature locations in a neutral and a “peak” frame
Shape Models • Predefined Shape Models • Setting the position of the landmarks manually • Active Shape Models (Cootes, 1995) • Using a set of manually labeled face image as a training set • PCA is used to reduce the dimensionality of the training set • During the training stage, for each landmarks their mean position and variance are determined
Localizing Facial Features • Using face detection techniques to detect facial features – local feature detectors (Cristinacce, Cootes, 2004) • Normalised correlation • BCD • Shape models can be used to reduce the number of false detections by only selecting plausible configurations of feature matches • With shape models the approximate location and orientation of the face can be used to perform a local search of facial features.
Shape Search • Unconstrained search • A bounding box of every feature are calculated • The feature detector is then applied to each region • The best match of is detector is taken • Constrained search – Combinatoric Shape Search (CSS) • Few of best responses are taken from each local detector • All combinations of feature locations are checked
Next Steps… • Previous method works fine on neutral faces • The same method can be modified to work on faces with emotions • Other approach for localizing facial features? • Color based • Intensity based (edges, thresholds)
