Neural Network-Based Face Detection
Abstract: We present a neural network-based upright frontal face detection system. A retinal 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 non face training examples, which must be chosen to span the entire space of non face 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.

Neural Network-Based Face Detection

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Introduction
In this paper, we present a neural network-based algorithm to detect upright, frontal views of faces
in gray-scale images1. The algorithm works by applying one or more neural networks directly to
portions of the input image, 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 probabilistic models of
the set of face images [4, 13, 15], or implicitly 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.



Description of the System
Our system operates in two stages: it first applies a set of neural network-based filters to an image,
and then uses an arbitrator to combine the outputs. The filters examine each location in the image at
several scales, looking for locations that might contain a face. The arbitrator then merges detections
from individual filters and eliminates overlapping detections.

2.1 Stage One: A Neural Network-Based Filter
The first component of our system is a filter that receives as input a 20x20 pixel region of the
image, and generates an output ranging from 1 to -1, signifying the presence or absence of a face,
respectively. To detect faces anywhere in the input, the filter is applied at every location in the
image. To detect faces larger than the window size, the input image is repeatedly reduced in size
(by subsampling), and the filter is applied at each size. This filter must have some invariance to
position and scale. The amount of invariance determines the number of scales and positions at
which it must be applied. For the work presented here, we apply the filter at every pixel position
in the image, and scale the image down by a factor of 1.2 for each step in the pyramid.


2.2 Stage Two: Merging Overlapping Detections and Arbitration
The examples in Fig. 3 showed that the raw output from a single network will contain a number of
false detections. In this section, we present two strategies to improve the reliability of the detector:
merging overlapping detections from a single network and arbitrating among multiple networks.

2.2.1 Merging Overlapping Detections
Note that in Fig. 3, most faces are detected at multiple nearby positions or scales, while false detections
often occur with less consistency. This observation leads to a heuristic which can eliminate
many false detections. For each location and scale, the number of detections within a specified
neighborhood of that location can be counted. If the number is above a threshold, then that location
is classified as a face. The centroid of the nearby detections defines the location of the
detection result, thereby collapsing multiple detections. In the experiments section, this heuristic
will be referred to as “thresholding”.

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