Detecting Faces in Images : A Survey <s> Face Image Database <s> We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases. <s> BIB001 </s> Detecting Faces in Images : A Survey <s> Face Image Database <s> A repertory dialler has a circulating dynamic store for the storage of the information required for impulsing to line upon operation of a selected call push-button. The push-buttons are each associated with an individual data word in the store and all the data words normally circulate round the store as a single block of data without any gaps between words. There is provision for new data words to be inserted and for existing data words to be erased. Upon erasure of a word that produces a gap in the circulating data block, that gap is reduced by temporarily varying the effective length of the loop of the circulating store during each of a succession of passes of the data block until the gap is closed up. <s> BIB002 </s> Detecting Faces in Images : A Survey <s> Face Image Database <s> Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance. <s> BIB003
Although many face detection methods have been proposed, less attention has been paid to the development of an image database for face detection research. The FERET database consists of monochrome images taken in different frontal views and in left and right profiles BIB003 . Only the upper torso of an individual (mostly head and necks) appears in an image on a uniform and uncluttered background. The FERET database has been used to assess the strengthens and weaknesses of different face recognition approaches BIB003 . Since each image consists of an individual on a uniform and uncluttered background, it is not suitable for face detection benchmarking. This is similar to many databases that were created for the development and testing of face recognition algorithms.  . The images were taken at different times, varying the lighting, facial expressions, and facial details (glasses). The Harvard database consists of cropped, masked frontal face images taken from a wide variety of light sources  . It was used by Hallinan for a study on face recognition under the effect of varying illumination conditions. With 16 individuals, the Yale face database (available at http://cvc.yale.edu/) contains 10 frontal images per person, each with different facial expressions, with and without glasses, and under different lighting conditions BIB001 . The M2VTS multimodal database from the European ACTS projects was developed for access control experiments using multimodal inputs BIB002 . It contains sequences of face images of 37 people. The five sequences for each subject were taken over one week. Each image sequence contains images from right profile (-90 degree) to left profile (90 degree) while the subject counts from "0" to "9" in their native languages. The UMIST database consists of 564 images of 20 people with varying pose. The images of each subject cover a range of poses from right profile to frontal views  . The Purdue AR database contains over 3,276 color images of 126 people (70 males and 56 females) in frontal view  . This database is designed for face recognition experiments under several mixing factors, such as facial expressions, illumination conditions, and occlusions. All the faces appear with different facial expression (neutral, smile, anger, and scream), illumination (left light source, right light source, and sources from both sides), and occlusion (wearing sunglasses or scarf). The images were taken during two sessions separated by two weeks. All the images were taken by the same camera setup under tightly controlled conditions of illumination and pose. This face database has been applied to image and video indexing as well as retrieval  . Table 2 summarizes the characteristics of the abovementioned face image databases.