A novel learning approach for human face detection using a network of linear units is presented. The SNoW learning architecture is a sparse network of linear functions over a pre-defined or incrementally learned feature space and is specifically tailored for learning in the presence of a very large number of features. A wide range of face images in different poses, with different expressions and under different lighting conditions are used as a training set to capture the variations of human faces. Experimental results on commonly used benchmark data sets of a wide range of face images show that the SNoW-based approach outperforms methods that use neural networks, Bayesian methods, support vector machines and others. Furthermore, learning and evaluation using the SNoW-based method are significantly more efficient than with other methods.

Eigenceface or Principal Component Analysis (PCA) methods have demonstrated their success in face recognition, detection, and tracking. The representation in PCA is based on the second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics (HOS) have been used as a more informative low dimensional representation than PCA for face and vehicle detection. In this paper we investigate a generalization of PCA, Kernel Principal Component Analysis (Kernel PCA), for learning low dimensional representations in the context of face recognition. In contrast to HOS, Kernel PCA computes the higher order statistics without the combinatorial explosion of time and memory complexity. While PCA aims to nd a second order correlation of patterns, Kernel PCA provides a replacement which takes into account higher order correlations. We compare the recognition results using kernel met...

Human faces provide enormous information and a friendly interface in intelligent human computer interaction. This has motivated a very active research area on, among others, face recognition, face tracking, pose estimation, expression recognition and gesture recognition. However, most existing methods on these topics assume human faces in an image or a image sequence have been identied and localized. To build a fully automated system that analyzes information of human faces, it is essential to develop robust and eÆcient algorithms to detect human faces. Given a single or a sequence of images, the goal of face detection is to identify and locate human faces regardless of their positions, scales, orientations and lighting conditions. Such problem is challenging because human faces are highly non-rigid objects with a high degree of variability in size, shape, color and texture. The purpose of this paper is to give a critical survey of existing techniques on face detection which has attra...

Principal Component Analysis and Fisher Linear Discriminant methods have demonstrated their success in face detection, recognition, and tracking. The representation in these subspace methods is based on second order statistics of the image set, and does not address higher order statistical dependencies such as the relationships among three or more pixels. Recently Higher Order Statistics and Independent Component Analysis (ICA) have been used as informative low dimensional representations for visual recognition.

We present two methods using mixtures of linear subspaces for face detection in gray level images. One method uses a mixture of factor analyzers to concurrently perform clustering and, within each cluster, perform local dimensionality reduction. The parameters of the mixture model are estimated using an EM algorithm. A face is detected if the probability of an input sample is above a predened threshold. The other mixture of subspaces method uses Kohonen 's self-organizing map for clustering and Fisher Linear Discriminant to nd an optimal projection and a Gaussian distribution to model the class-conditional density function of the projected samples for each class. The parameters of the class-conditional density functions are maximum likelihood estimates and the decision rule is also based on maximum likelihood. A wide range of face images including ones in dierent poses, with dierent expressions and under dierent lighting conditions are used as the training set to capture the varia...

In this paper we propose a new classifier- the Maximal Rejection Classifier (MRC)- for target detection. Unlike pattern recognition, pattern detection problems require a separation between two classes, Target and Clutter, where the probability of the former is substantially smaller, compared to that of the latter. The MRC is a linear classifier, based on successive rejection operations. Each rejection is performed using a projection followed by thresholding. In contrast to common classifiers the projection vector is influenced by the probabilities of obtaining target or clutter signals. The projection vector is designed to minimize the expected number of operations until detection. In the case where the probabilities of target and clutter signals are equal, it is shown that the Fisher linear discriminant is optimal in the above sense. However, in more common cases where the probablitities are quite different, a new optimal classifier is suggested. An application of detecting frontal faces in images is demonstrated using the MRC with encouraging results.

This paper describes a method for the detection and tracking of human face and facial features. Skin segmentation is learnt from samples of an image. After detecting a moving object , the corresponding area is searched for clusters of pixels with a known distribution. Since we only use the hue (color) component this process is quite insensitive to illumination changes. The face localization procedure looks for areas in the segmented area which resemble a head. Using simple heuristics, the located head is searched and its centroid is fed back to a camera motion control algorithm which tries to keep the face centered in the image using a pan-tilt camera unit. Furthermore the system is capable of tracking, in every frame, the three main features of a human face. Since precise eye location is computationally intensive, an eye and mouth locator using fast morphological and linear filters is developed. This allows for frame-byframe checking, which reduces the probability of tracking a non ba...

We present two methods using multimodal density models for face detection in gray-level images. One generative method uses a mixture of factor analyzers to concurrently perform clustering and, within each cluster, perform local dimensionality reduction. The parameters of the mixture model are estimated using the EM algorithm. A face is detected if the probability of an input sample is above a predefined threshold. The other discriminative method uses Kohonen’s self-organizing map for clustering, Fisher’s linear discriminant to find an optimal projection for pattern classification, and a Gaussian distribution to model the class-conditional density function of the projected samples for each class. The parameters of the class-conditional density functions are maximum likelihood estimates, and the decision rule is also based on maximum likelihood. A wide range of face images including ones in different poses, with different expressions and under different lighting conditions, is used as the training set to capture variations of the human face. Our methods have been tested on three data sets with a total of 225 images containing 871 faces. Experimental results on the first two data sets show that our generative and discriminative methods perform as well as the best methods in the literature, yet have fewer false detections. Meanwhile, both methods are able to detect faces of nonfrontal views and under more extreme lighting in the third data set. c ○ 2001 Elsevier Science (USA) Key Words: face detection; multimodal density estimation; Fisher’s linear discriminant, self-organizing map; factor analysis; mixture of factor analyzers; EM algorithm. 1.

We present a probabilistic method to detect human faces using a mixture of factor analyzers. One characteristic of this mixture model is that it concurrently performs clustering and, within each cluster, local dimensionality reduction. A wide range of face images including ones in dierent poses, with dierent expressions and under dierent lighting conditions are used as the training set to capture the variations of human faces. In order to t the mixture model to the sample face images, the parameters are estimated using an EM algorithm. Experimental results show that faces in dierent poses, with dierent facial expressions, and under dierent lighting conditions are accurately detected by our method. 1. INTRODUCTION Images of human faces are central to intelligent human computer interaction. Much research is being done involving face images, including face recognition, face tracking, pose estimation, expression recognition and gesture recognition. However, most existing methods o...

In this paper, we address the problem of face detection in still images. We propose a fast algorithm for detecting human faces in color images. The algorithm uses color histogram for skin (in the HSV space) in conjunction with edge information to quickly locate faces in a given image. The proposed algorithm has been tested on various real images and its performance is found to be quite satisfactory.