Face Detection Using Machine Learning
First Claim
1. A method implemented on a computer system to automatically train a convolutional neural network (CNN) on face detection, the training method using a pool containing face images and a significantly larger number of non-face images, the training method occurring in stages, the stages comprising:
- formulating a training set for the stage, the training set having an approximately equal number of face images and non-face images derived from the images in the pool, the non-face images including non-face images that were false positives in earlier stages; and
training the CNN using the training set for that stage.
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Abstract
A disclosed face detection system (and method) is based on a structure of a convolutional neural network (CNN). One aspect concerns a method for automatically training a CNN for face detection. The training is performed such that balanced number of face images and non-face images are used for training by deriving additional face images from the face images. The training is also performed by adaptively changing a number of trainings of a stage according to automatic stopping criteria. Another aspect concerns a system for performing image detection by integrating data at different scales (i.e., different image extents) for better use of data in each scale. The system may include CNNs automatically trained using the method disclosed herein.
75 Citations
22 Claims
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1. A method implemented on a computer system to automatically train a convolutional neural network (CNN) on face detection, the training method using a pool containing face images and a significantly larger number of non-face images, the training method occurring in stages, the stages comprising:
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formulating a training set for the stage, the training set having an approximately equal number of face images and non-face images derived from the images in the pool, the non-face images including non-face images that were false positives in earlier stages; and training the CNN using the training set for that stage. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19)
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20. A non-transitory computer readable medium configured to store program code comprised of instructions, the instructions when executed by a processor cause the processor to train a convolutional neural network (CNN) on face detection, the training method using a pool containing face images and a significantly larger number of non-face images, the training method occurring in stages, the stages comprising:
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formulating a training set for the stage, the training set having an approximately equal number of face images and non-face images derived from the images in the pool, the non-face images including non-face images that were false positives in earlier stages; and training the CNN using the training set for that stage.
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21. A system to automatically train a convolutional neural network (CNN) on face detection, the training method using a pool containing face images and a significantly larger number of non-face images, the training method occurring in stages, the stages comprising:
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formulating a training set for the stage, the training set having an approximately equal number of face images and non-face images derived from the images in the pool, the non-face images including non-face images that were false positives in earlier stages; and training the CNN using the training set for that stage.
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22-34. -34. (canceled)
Specification