Detecting Faces in Images : A Survey <s> Inductive Learning <s> The paper proposes a novel algorithm for face detection using decision trees (DT) and shows its generality and feasibility using a database consisting of 2340 face images from the FERET database (corresponding to 817 subjects and including 190 sets of duplicates) over a semi-uniform background. The approach used for face detection involves three main stages, those of location, cropping, and post-processing. The first stage finds a rough approximation for the possible location of the face box, the second stage will refine it, and the last stage decider whether a face is present in the image and if the answer is positive would normalize the face image. The algorithm does not require multiple (scale) templates and the accuracy achieved is 96%. Accuracy is based on the visual observation that the face box includes both eyes, nose, and mouth, and that the top side of the box is below the hairline. Experiments were also performed to assess the accuracy of the algorithm in rejecting images where no face is present. Using a small database of 25 images of various but complex backgrounds the algorithm failed on two images for an overall accuracy rate of 92%. <s> BIB001 </s> Detecting Faces in Images : A Survey <s> Inductive Learning <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> BIB002 </s> Detecting Faces in Images : A Survey <s> Inductive Learning <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> BIB003
Inductive learning algorithms have also been applied to locate and detect faces. Huang et al. applied Quinlan's C4.5 algorithm  to learn a decision tree from positive and negative examples of face patterns BIB001 . Each training example is an 8 Â 8 pixel window and is represented by a vector of 30 attributes which is composed of entropy, mean, and standard deviation of the pixel intensity values. From these examples, C4.5 builds a classifier as a decision tree whose leaves indicate class identity and whose nodes specify tests to perform on a single attribute. The learned decision tree is then used to decide whether a face exists in the input example. The experiments show a localization accuracy rate of 96 percent on a set of 2,340 frontal face images in the FERET data set. Duta and Jain  presented a method to learn the face concept using Mitchell's Find-S algorithm  . Similar to BIB002 , they conjecture that the distribution of face patterns pðxjfaceÞ can be approximated by a set of Gaussian clusters and that the distance from a face instance to one of the cluster centroids should be smaller than a fraction of the maximum distance from the points in that cluster to its centroid. The Find-S algorithm is then applied to learn the thresholding distance such that faces and nonfaces can be differentiated. This method has several distinct characteristics. First, it does not use negative (nonface) examples, while BIB002 , BIB003 use both positive and negative examples. Second, only the central portion of a face is used for training. Third, feature vectors consist of images with 32 intensity levels or textures, while BIB002 uses full-scale intensity values as inputs. This method achieves a detection rate of 90 percent on the first CMU data set.