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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 1, JANUARY 1998 23 Neural Network-Based Face Detection Henry A. Rowley, Student Member, IEEE, Shumeet Baluja, and Takeo Kanade, Fellow, IEEE Abstract —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. Index Terms —Face detection, pattern recognition, computer vision, artificial neural networks, machine learning. —————————— —————————— 1I NTRODUCTION N this paper, we present a neural network-based algo- rithm to detect upright, frontal views of faces in gray- scale images. 1 The algorithm works by applying one or more neural networks directly to portions of the input im- age and arbitrating their results. Each network is trained to output the presence or absence of a face. The algorithms and training methods are designed to be general, with little customization for faces. Many face detection researchers have used the idea that facial images can be characterized directly in terms of pixel intensities. These images can be characterized by probabil- istic models of the set of face images [4], [13], [15] or im- plicitly by neural networks or other mechanisms [3], [12], [14], [19], [21], [23], [25], [26]. The parameters for these models are adjusted either automatically from example images (as in our work) or by hand. A few authors have taken the approach of extracting features and applying ei- ther manually or automatically generated rules for evalu- ating these features [7], [11]. Training a neural network for the face detection task is challenging because of the difficulty in characterizing proto- typical “nonface” images. Unlike face recognition , in which the classes to be discriminated are different faces, the two classes to be discriminated in face detection are “images con- taining faces” and “images not containing faces.” It is easy to 1. An interactive demonstration of the system is available on the World Wide Web at http://www.cs.cmu.edu~har/faces.html , which allows anyone to submit images for processing by the face detector, and to see the detection
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