Detecting Faces in Images : A Survey <s> Discussion <s> Abstract An experiment on line extraction and pattern detection in a photograph is performed. From digitized pictures which have eight gray levels, the value and direction of the steepest descent is first calculated for each point with 3 × 3 meshes. Then those points whose gradient is more than certain threshold value are regarded as component points of line segments. From these points the estimation is done to extend the line portions as far as possible to the neighbouring ones. The pattern detection is done mainly for the photographs of human faces which are in front direction. A standard pattern of a front face is stored in a computer and the correlation values with several sizes of the standard pattern are calculated for the face contour, eyes, nose, and mouth independently. From these correlation values of the face portions, the final determination is done for the existence of a face. This two stage recognition process is adopted because certain portions of a face may be enough for the decision even if the other portion of a face is hidden in noise and could not be detected as such. For many photographs of human faces in several conditions the whole algorithm has been tested with considerable success. <s> BIB001 </s> Detecting Faces in Images : A Survey <s> Discussion <s> We describe a computer program which understands a greyscale image of a face well enough to locate individual face features such as eyes and mouth. The program has two distinct components: modules designed to locate particular face features, usually in a restricted area; and the overall control strategy which activates modules on the basis of the current solution state, and assesses and integrates the results of each module. <s> BIB002 </s> Detecting Faces in Images : A Survey <s> Discussion <s> An algorithm for locating quasi-frontal views of human faces in cluttered scenes is presented. The algorithm works by coupling a set of local feature detectors with a statistical model of the mutual distances between facial features it is invariant with respect to translation, rotation (in the plane), and scale and can handle partial occlusions of the face. On a challenging database with complicated and varied backgrounds, the algorithm achieved a correct localization rate of 95% in images where the face appeared quasi-frontally. > <s> BIB003 </s> Detecting Faces in Images : A Survey <s> Discussion <s> In this paper, we describe an algorithm for object recognition that explicitly models and estimated the posterior probability function, P(object/image). We have chosen a functional form of the posterior probability function that captures the joint statistics of local appearance and position on the object as well as the statistics of local appearance in the visual world at large. We use a discrete representation of local appearance consisting of approximately 10/sup 6/ patterns. We compute an estimate of P(object/image) in closed form by counting the frequency of occurrence of these patterns over various sets of training images. We have used this method for detecting human faces from frontal and profile views. The algorithm for frontal views has shown a detection rate of 93.0% with 88 false alarms on a set of 125 images containing 483 faces combining the MIT test set of Sung and Poggio with the CMU test sets of Rowley, Baluja, and Kanade. The algorithm for detection of profile views has also demonstrated promising results. <s> BIB004 </s> Detecting Faces in Images : A Survey <s> Discussion <s> We present an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face" and "nonface" model clusters. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines, based on the difference feature vector measurements, whether or not a human face exists at the current image location. We show empirically that the distance metric we adopt for computing difference feature vectors, and the "nonface" clusters we include in our distribution-based model, are both critical for the success of our system. <s> BIB005 </s> Detecting Faces in Images : A Survey <s> Discussion <s> We present a neural network-based upright frontal face detection system. A retinally connected neural network examines small windows of an image and decides whether each window contains a face. The system arbitrates between multiple networks to improve performance over a single network. We present a straightforward procedure for aligning positive face examples for training. To collect negative examples, we use a bootstrap algorithm, which adds false detections into the training set as training progresses. This eliminates the difficult task of manually selecting nonface training examples, which must be chosen to span the entire space of nonface images. Simple heuristics, such as using the fact that faces rarely overlap in images, can further improve the accuracy. Comparisons with several other state-of-the-art face detection systems are presented, showing that our system has comparable performance in terms of detection and false-positive rates. <s> BIB006 </s> Detecting Faces in Images : A Survey <s> Discussion <s> This paper presents an approach to object detection which is based on recent work in statistical models for texture synthesis and recognition. Our method follows the texture recognition work of De Bonet and Viola (1998). We use feature vectors which capture the joint occurrence of local features at multiple resolutions. The distribution of feature vectors for a set of training images of an object class is estimated by clustering the data and then forming a mixture of Gaussian models. The mixture model is further refined by determining which clusters are the most discriminative for the class and retaining only those clusters. After the model is learned, test images are classified by computing the likelihood of their feature vectors with respect to the model. We present promising results in applying our technique to face detection and car detection. <s> BIB007 </s> Detecting Faces in Images : A Survey <s> Discussion <s> A method for detecting and describing the features of faces using deformable templates is described. The feature of interest, an eye for example, is described by a parameterized template. An energy function is defined which links edges, peaks, and valleys in the image intensity to corresponding properties of the template. The template then interacts dynamically with the image, by altering its parameter values to minimize the energy function, thereby deforming itself to find the best fit. The final parameter values can be used as descriptors for the features. This method is demonstrated by showing deformable templates detecting eyes and mouths in real images. > <s> BIB008 </s> Detecting Faces in Images : A Survey <s> Discussion <s> The human face is an object that is easily located in complex scenes by infants and adults alike. Yet the development of an automated system to perform this task is extremely challenging. An attempt to solve this problem raises two important issues in object location. First, natural objects such as human faces tend to have boundaries which are not exactly described by analytical functions. Second, the object of interest (face) could occur in a scene in various sizes, thus requiring the use of scale independent techniques which can detect instances of the object at all scales.Although, the task of identifying a well-framed face (as one of a set of labeled faces) has been well researched, the task of locating a face in a natural scene is relatively unexplored. We present a computational theory for locating human faces in scenes with certain constraints. The theory will be validated by experiments confined to instances where people's faces are the primary subject of the scene, occlusion is minimal, and the faces contrast well against the background. <s> BIB009
We have reviewed and classified face detection methods into four major categories. However, some methods can be classified into more than one category. For example, template matching methods usually use a face model and subtemplates to extract facial features BIB001 ,  , BIB008 ,  , BIB009 , and then use these features to locate or detect faces. Furthermore, the boundary between knowledge-based methods and some template matching methods is blurry since the latter usually implicitly applies human knowledge to define the face templates BIB001 , BIB002 ,  . On the other hand, face detection methods can also be categorized otherwise. For example, these methods can be classified based on whether they rely on local features BIB003 , BIB004 , BIB007 or treat a face pattern as whole (i.e., holistic) BIB005 , BIB006 . Nevertheless, we think the four major classes categorize most methods sufficiently and appropriately.