Detecting Faces in Images : A Survey <s> Knowledge-Based Top-Down Methods <s> Face detection is a key problem in building automated systems that perform face recognition. A very attractive approach for face detection is based on multiresolution images (also known as mosaic images). Motivated by the simplicity of this approach, a rule-based face detection algorithm in frontal views is developed that extends the work of G. Yang and T.S. Huang (see Pattern Recognition, vol.27, no.1, p.53-63, 1994). The proposed algorithm has been applied to frontal views extracted from the European ACTS M2VTS database that contains the videosequences of 37 different persons. It has been found that the algorithm provides a correct facial candidate in all cases. However, the success rate of the detected facial features (e.g. eyebrows/eyes, nostrils/nose, and mouth) that validate the choice of a facial candidate is found to be 86.5% under the most strict evaluation conditions. <s> BIB001 </s> Detecting Faces in Images : A Survey <s> Knowledge-Based Top-Down Methods <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
In this approach, face detection methods are developed based on the rules derived from the researcher's knowledge of human faces. It is easy to come up with simple rules to describe the features of a face and their relationships. For example, a face often appears in an image with two eyes that are symmetric to each other, a nose, and a mouth. The relationships between features can be represented by their relative distances and positions. Facial features in an input image are extracted first, and face candidates are identified based on the coded rules. A verification process is usually applied to reduce false detections. One problem with this approach is the difficulty in translating human knowledge into well-defined rules. If the rules are detailed (i.e., strict), they may fail to detect faces that do not pass all the rules. If the rules are too general, they may give many false positives. Moreover, it is difficult to extend this approach to detect faces in different poses since it is challenging to enumerate all possible cases. On the other hand, heuristics about faces work well in detecting frontal faces in uncluttered scenes. Yang and Huang used a hierarchical knowledge-based method to detect faces  . Their system consists of three levels of rules. At the highest level, all possible face candidates are found by scanning a window over the input image and applying a set of rules at each location. The rules at a higher level are general descriptions of what a face looks like while the rules at lower levels rely on details of facial features. A multiresolution hierarchy of images is created by averaging and subsampling, and an example is shown in Fig. 1 . Examples of the coded rules used to locate face candidates in the lowest resolution include: "the center part of the face (the dark shaded parts in Fig. 2 ) has four cells with a basically uniform intensity," "the upper round part of a face (the light shaded parts in Fig. 2 ) has a basically uniform intensity," and "the difference between the average gray values of the center part and the upper round part is significant." The lowest resolution (Level 1) image is searched for face candidates and these are further processed at finer resolutions. At Level 2, local histogram equalization is performed on the face candidates received from Level 2, followed by edge detection. Surviving candidate regions are then examined at Level 3 with another set of rules that respond to facial features such as the eyes and mouth. Evaluated on a test set of 60 images, this system located faces in 50 of the test images while there are 28 images in which false alarms appear. One attractive feature of this method is that a coarse-to-fine or focus-of-attention strategy is used to reduce the required computation. Although it does not result in a high detection rate, the ideas of using a multiresolution hierarchy and rules to guide searches have been used in later face detection works BIB001 . Kotropoulos and Pitas BIB001 presented a rule-based localization method which is similar to  and  . First, facial features are located with a projection method that Kanade successfully used to locate the boundary of a face  . Let Iðx; yÞ be the intensity value of an m Â n image at position ðx; yÞ, the horizontal and vertical projections of the image are defined as HIðxÞ ¼ P n y¼1 Iðx; yÞ and V IðyÞ ¼ P m x¼1 Iðx; yÞ. The horizontal profile of an input image is obtained first, and then the two local minima, determined by detecting abrupt changes in HI, are said to correspond to the left and right side of the head. Similarly, the vertical profile is obtained and the local minima are determined for the locations of mouth lips, nose tip, and eyes. These detected features constitute a facial candidate. Fig. 3a shows one example where the boundaries of the face correspond to the local minimum where abrupt intensity changes occur. Subsequently, eyebrow/eyes, nostrils/nose, and the mouth detection rules are used to validate these candidates. The proposed method has been tested using a set of faces in frontal views extracted from the European ACTS M2VTS (MultiModal Verification for Teleservices and Security applications) database BIB002 which contains video sequences of 37 different people. Each image sequence contains only one face in a uniform background. Their method provides correct face candidates in all tests. The detection rate is 86.5 percent if successful detection is defined as correctly identifying all facial features. Fig. 3b shows one example in which it becomes difficult to locate a face in a complex background using the horizontal and vertical profiles. Furthermore, this method cannot readily detect multiple faces as illustrated in Fig. 3c . Essentially, the projection method can be effective if the window over which it operates is suitably located to avoid misleading interference.