ature Extraction(2) Feature Extraction (2B Eigenface Eigenface(Cont Principal components are gained by training step, each aTraining set: global grayscale face images image in a training set is projected to eigenface subspace OFind the principal component of the distribution of faces DAlso called Principal Component Analysis(PCA), patented at MIT, currently used by Visage 's facePCA e. Select k eigenvectors that have the largest eigenvalues represent the most significant variation within the image t which are calle real faces in a Roughly translated as"one,s own eigenfaces THese k eigenfaces span a O Take advantage redundancy existing in the training set and represent it in called the"face space a more compact and meaningful way J Each image in the training variations of eigenface are frequently set can be represented as a used as basis of other face recognition methods linear combination of Feature Extraction(2) Feature Extraction(2 Eigenface(Cont Eigenface(Cont Initialization ach image in the training set: Compute the covariance matrix of this et of difference images; Compute the eigenvectors of the covariance 1. Acquire and align an nitial set of fa and translate such located at the same xperience, the eigenfaces coordinate be updated or recalculated Feature Extraction(2): Feature Extraction(2): Eigenface(Cont) Eigenface(Cont. 1. Calculate a set of weights based on the input image and the M 3. Calculate the corresponding distribution in k- eigenfaces by projecting the input image onto each of the dimensional weight space for each known individual, by projecting their face images onto the"face space. 2. Determine if the image is a face at all by checking to see if the nage is sufficiently close to face spa 3. If it is a face, classify the weight pattern as either a known person or as O Each training image can be represented by a k dimer O For 1-to-many identification, project the concerned face space and get a k dimensional vector, the ' live OA distance measure is used to compare the similarity en the live template and the training vectors 66 Biometrics Research Centre (UGC/CRC) Lecture 8 - 31 Feature Extraction (2): Feature Extraction (2): Eigenface Eigenface ‰Principal components are gained by training step, each image in a training set is projected to eigenface subspace ‰Also called Principal Component Analysis (PCA), patented at MIT, currently used by Viisage’s face recognition software ‰Roughly translated as “one’s own face” ‰Take advantage redundancy existing in the training set and represent it in a more compact and meaningful way ‰Variations of eigenface are frequently used as basis of other face recognition methods Biometrics Research Centre (UGC/CRC) Lecture 8 - 32 Feature Extraction (2): Feature Extraction (2): Eigenface Eigenface (Cont.) (Cont.) ‰Training set: global grayscale face images ‰Find the principal component of the distribution of faces, i.e. Select k eigenvectors that have the largest eigenvalues to represent the most significant variation within the image set, which are called eigenfaces ‰These k eigenfaces span a k-dimensional subspace, called the “face space” ‰Each image in the training set can be represented as a linear combination of eigenvectors Biometrics Research Centre (UGC/CRC) Lecture 8 - 33 Feature Extraction (2): Feature Extraction (2): Eigenface Eigenface (Cont.) (Cont.) Initialization 1. Acquire and align an initial set of face images (the training set) - Rotate, scale and translate such that the eyes are located at the same coordinates. Biometrics Research Centre (UGC/CRC) Lecture 8 - 34 Feature Extraction (2): Feature Extraction (2): Eigenface Eigenface (Cont.) (Cont.) 2. Compute the average face image; Compute the difference image for each image in the training set; Compute the covariance matrix of this set of difference images; Compute the eigenvectors of the covariance matrix ‰ Get the eigenfaces from the training set, keeping only the k images that correspond to the highest eigenvalues. These k images define the face space. As new faces are experienced, the eigenfaces can be updated or recalculated Biometrics Research Centre (UGC/CRC) Lecture 8 - 35 Feature Extraction (2): Feature Extraction (2): Eigenface Eigenface (Cont.) (Cont.) 3. Calculate the corresponding distribution in k￾dimensional weight space for each known individual, by projecting their face images onto the “face space.” Biometrics Research Centre (UGC/CRC) Lecture 8 - 36 Feature Extraction (2): Feature Extraction (2): Eigenface Eigenface (Cont.) (Cont.) 1. Calculate a set of weights based on the input image and the M eigenfaces by projecting the input image onto each of the eigenfaces. 2. Determine if the image is a face at all by checking to see if the image is sufficiently close to “face space.” 3. If it is a face, classify the weight pattern as either a known person or as unknown. 4. (Optional) Update the eigenfaces and/or weight patterns. ‰Each training image can be represented by a k dimensional vector ‰For 1-to-many identification, project the concerned image to the face space and get a k dimensional vector, the ‘live’ template ‰A distance measure is used to compare the similarity between the ‘live’ template and the training vectors