人脸识别的简单算法

Rowley-Baluja-Kanade Face Detector

Author: Scott Sanner

Contents

∙Introduction

∙Algorithm

∙Data Preparation

∙Training

∙Image Scanning

∙Testing

∙Conclusion

∙References

∙Software

Introduction

The goal of this project is to implement and analyze the

Rowley-Baluja-Kanade neural net face detector as described in [2] along with some enhancements for training and recognition proposed by Sung and Poggio as described in [3]. The basic goal underlying both approaches is to train a neural network or other recognition system on a labelled database of face and non-face images. This face classifier can then be used to scan over an image resolution pyramid to determine the locations and scaling of any faces (if present) and return them to the user.

Overall, the task of face recognition can be extremely difficult given the wide variety of faces to match, the presence of facial hair, variations in lighting and shadowing, and the possibility of angular, scaling, and dimensional variances. Consequently an ideal face detector should attempt to mitigate all of these problems while achieving a high detection rate and minimizing the number of false positives. As we will see in the latter requirement, there is a tradeoff between the positive detection rate and the false positive rate and the balance between the two will need to be evaluated by the individual user and application domain.

Algorithm Overview

To achieve the above goals for face detection, we use a general algorithm that is a straightforward application of data preparation, training, and image scanning. This algorithm is outlined below:

Normalize Training Data:

- For each face and non-face image:

- Subtract out an approximation of the shading plane

to correct for single light source effects

- Rescale histogram so that every image has the same

same gray level range

- Aggregate data into labeled data sets

Train Neural Net:

- Until the Neural Net reaches convergence (or a decrease

in performance on the validation set):

- Perform gradient descent error backpropagation on

on the neural net for the batch of all training data

Apply Face Detector to Image:

- Build a resolution pyramid of the image by successively

successively decreasing the image resolution at each

level of the pyramid, stopping at some default minimum

resolution

- For each level of the pyramid

- Scan over the image, applying the trained neural net

face detector to each rectangle within the image

- If a positive face classification is found for a

rectangle, scale this rectangle to the size

appropriate for the original image and add it to

the face bounding-box set