Face Recognition: An Introduction

Face Recognition Face is the most common biometric used by humans Applications range from static, mug-shot verification to a dynamic, uncontrolled face identification in a cluttered background Challenges: automatically locate the face recognize the face from a general view point under different illumination conditions, facial expressions, and aging effects O O Face recognition can be categorized into appearance-based, geometry-based, and hybrid approaches.

Authentication vs Identification Face Authentication/Verification (1:1 matching) Face Identification/Recognition (1:N matching)

Applications  Access Control www.viisage.com www.visionics.com

Applications Face Scan at Airports  Video Surveillance (On-line or off-line) Face Scan at Airports www.facesnap.de

Face Recognition Application

Why is Face Recognition Hard? Many Faces of the same person

Face Recognition Difficulties Identify similar faces (inter-class similarity) Accommodate intra-class variability due to: head pose illumination conditions expressions facial accessories aging effects O O Face recognition can be categorized into appearance-based, geometry-based, and hybrid approaches. Cartoon faces

Inter-class Similarity Different persons may have very similar appearance www.marykateandashley.com news.bbc.co.uk/hi/english/in_depth/americas/2000/us_elections O O Face recognition can be categorized into appearance-based, geometry-based, and hybrid approaches. Twins Father and son

Intra-class Variability Faces with intra-subject variations in pose, illumination, expression, accessories, color, occlusions, and brightness O O Face recognition can be categorized into appearance-based, geometry-based, and hybrid approaches.

Sketch of a Pattern Recognition Architecture Feature Extraction Classification Image (window) Object Identity Feature Vector

Example: Face Detection Scan window over image Classify window as either: Face Non-face Classifier Window Face Non-face

Example: Finding skin Skin has a very small range of (intensity independent) colors, and little texture Compute an intensity-independent color measure, check if color is in this range, check if there is little texture (median filter) See this as a classifier - we can set up the tests by hand, or learn them. get class conditional densities (histograms), priors from data (counting) Classifier is

Face Detection

Face Detection Algorithm Lighting Compensation Color Space Transformation Skin Color Detection Input Image Variance-based Segmentation Connected Component & Grouping Face Localization Eye/ Mouth Detection O O Face recognition can be categorized into appearance-based, geometry-based, and hybrid approaches. Face Boundary Detection Verifying/ Weighting Eyes-Mouth Triangles Facial Feature Detection Output Image

Face Recognition: 2-D and 3-D Time (video) 2-D Face Database 2-D Recognition Data Recognition Comparison 3-D 3-D Play this, it’s animated – Green denotes topics covered subsequent slides Prior knowledge of face class

Taxonomy of Face Recognition Algorithms Pose-dependency Pose-dependent Pose-invariant Viewer-centered Images Object-centered Models Face representation Matching features Appearance-based (Holistic) Hybrid Feature-based (Analytic)

Image as a Feature Vector x 1 2 3 Consider an n-pixel image to be a point in an n-dimensional space, x Rn. Each pixel value is a coordinate of x.

Nearest Neighbor Classifier { Rj } are set of training images. x 1 2 3 R1 R2 I

Comments Sometimes called “Template Matching” Variations on distance function (e.g. L1, robust distances) Multiple templates per class- perhaps many training images per class. Expensive to compute k distances, especially when each image is big (N dimensional). May not generalize well to unseen examples of class. Some solutions: Bayesian classification Dimensionality reduction

Eigenfaces A set of eigenfaces can be generated by performing a mathematical process called principal component analysis (PCA) on a large set of images depicting different human faces. Informally, eigenfaces can be considered a set of "standardized face ingredients", derived from statistical analysis )(covariance matrix ) of many pictures of faces. Any human face can be considered to be a combination of these standard faces.

Eigenfaces For example, one's face might be composed of the average face plus 10% from eigenface 1, 55% from eigenface 2, and even -3% from eigenface 3. The eigenfaces that are created will appear as light and dark areas that are arranged in a specific pattern.

Eigenfaces These eigenfaces can now be used to represent both existing and new faces: we can project a new (mean-subtracted) image on the eigenfaces and thereby record how that new face differs from the mean face. The eigenvalues associated with each eigenface represent how much the images in the training set vary from the mean image in that direction.

Eigenfaces We lose information by projecting the image on a subset of the eigenvectors, but we minimize this loss by keeping those eigenfaces with the largest eigenvalues. For instance, if we are working with a 100 x 100 image, then we will obtain 10,000 eigenvectors. In practical applications, most faces can typically be identified using a projection on between 100 and 150 eigenfaces, so that most of the 10,000 eigenvectors can be discarded.

How do you construct Eigenspace? [ ] [ ] [ x1 x2 x3 x4 x5 ] W Construct data matrix by stacking vectorized images and then apply Singular Value Decomposition (SVD)

Eigenfaces

Eigenfaces Modeling Recognition Given a collection of n labeled training images, Compute mean image and covariance matrix. Compute k Eigenvectors (note that these are images) of covariance matrix corresponding to k largest Eigenvalues. Project the training images to the k-dimensional Eigenspace. Recognition Given a test image, project to Eigenspace. Perform classification to the projected training images.

Eigenfaces: Training Images

Eigenfaces Mean Image Basis Images

Difficulties with PCA Projection may suppress important detail smallest variance directions may not be unimportant Method does not take discriminative task into account typically, we wish to compute features that allow good discrimination not the same as largest variance

Fisherfaces: Class specific linear projection An n-pixel image xRn can be projected to a low-dimensional feature space yRm by y = Wx where W is an n by m matrix. Recognition is performed using nearest neighbor in Rm. How do we choose a good W?

PCA & Fisher’s Linear Discriminant Between-class scatter Within-class scatter Total scatter Where c is the number of classes i is the mean of class i | i | is number of samples of i.. 1 2  1 2 Use this slide to explain the three types of scatter

PCA & Fisher’s Linear Discriminant PCA (Eigenfaces) Maximizes projected total scatter Fisher’s Linear Discriminant Maximizes ratio of projected between-class to projected within-class scatter 2 1 Point to emphasize is that PCA maximizes the projected total scatter – I.e., it presserves the information in the training set, and is optimal in a least squares sense for reconstruction. But in dropping dimensions, it may smear classes together. FLD trades off two desirable effects for recognition. A. The within class scatter over all classes is minimized – this makes classification using nearest neighbor effective. B. The between class scatter is maximized – this causes classes to be far apart in the feature space. FLD

Four Fisherfaces Database

Eigenfaces and Fisherfaces