Face detection

Patent metadata

Field	Value
Publication number	US-9852492-B2
Application number	US-201514859040-A
Country	US
Kind code	B2
Filing date	Sep 18, 2015
Priority date	Sep 18, 2015
Publication date	Dec 26, 2017
Grant date	Dec 26, 2017

Abstract

Briefly, embodiments of methods and/or systems of detecting and image of a human face in a digital image are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be refined by a neural network to generate signal sample value levels corresponding to probability that a human face may be depicted at a localized region of a digital image.

First claim

What is claimed is:

1. A method, comprising:
training parameters of a convolutional neural network (CNN) to label everyday objects; and
refining the parameters of the CNN, trained to label the everyday objects, to perform multi-aspect facial detection with respect to one or more images, the CNN being modified to exclude fully-connected layers.

2. The method of claim 1 , wherein multi-aspect facial images comprise: off-axis facial images, partially occluded facial images, cropped facial images, tilted facial images, or any combination thereof.

3. The method of claim 1 , further comprising training, prior to refining and modifying to exclude fully-connected layers, the CNN utilizing a set of training input image sample values depicting the everyday objects.

4. The method of claim 3 , further comprising:
modifying the set of training input image sample values to approach a threshold percentage of positive facial samples.

5. The method of claim 4 , wherein a threshold percentage of positive facial samples approaches a value of approximately 20.0% to 50.0%.

6. The method of claim 1 , further comprising:
executing the refining until a facial detection percentage is achieved.

7. The method of claim 1 , further comprising:
scaling signal sample values corresponding to an input image to at least approximately correspond with a detector window size.

8. The method of claim 1 , further comprising:
back projecting one or more output signal sample values of a subsequent convolutional filtering layer of the CNN to an original image.

9. The method of claim 1 , further comprising:
displaying a heat map corresponding to one or more localized regions at which a depiction of one or more human faces is likely to be present in an input image.

10. The method of claim 1 , wherein the CNN comprises an eight-layer deep CNN.

11. The method of claim 1 , wherein the modifying of the CNN comprises excluding three fully-connected layers.

12. An apparatus, comprising:
one or more processors to:
train parameters of a convolutional neural network (CNN) to label everyday objects; and
refine parameters of the CNN, trained to label the everyday objects, to perform multi-aspect facial detection with respect to one or more images, the CNN modified to exclude fully-connected layers.

13. The apparatus of claim 12 , wherein multi-aspect facial images comprise off-axis facial images, partially occluded facial images, cropped facial images, tilted facial images, or any combination thereof.

14. The apparatus of claim 12 , wherein the one or more processors are additionally to:
train the CNN to classify input image signal samples depicting the everyday objects before the CNN is modified to exclude fully-connected layers.

15. The apparatus of claim 12 , wherein the one or more processors are additionally to:
scale signal sample values corresponding to an input image to at least approximately correspond with a detector window size.

16. The apparatus of claim 12 , wherein the one or more processors are additionally to:
back project one or more output signal samples of a subsequent convolutional filtering of the CNN to an original image.

17. An apparatus comprising:
means for training parameters of a convolutional neural network (CNN) to label everyday objects; and
means for refining the parameters of the CNN, trained to label the everyday objects, to perform multi-aspect facial detection, wherein the means for refining comprises means for modifying the CNN to exclude fully-connected layers.

18. The apparatus of claim 17 , wherein the means for refining the CNN to perform multi-aspect facial detection comprises means for detecting off-axis facial images, partially occluded facial images, cropped facial images, tilted facial images, or any combination thereof.

19. The apparatus of claim 18 , further comprising:
means for scaling signal sample values corresponding to an input image to at least approximately correspond with a detector window size.

20. The apparatus of claim 18 , further comprising:
means for back projecting one or more output signal sample values of a subsequent convolutional filtering layer of the CNN to an original image.

Assignees

• Yahoo Holdings Inc

Inventors

• Saberian Mohammad
• Farfade Sachin Sudhakar
• Li Jia

Classifications

• G06T3/40 Primary

  Scaling of whole images or parts thereof, e.g. expanding or contracting · CPC title

• G06V10/82 Primary

  using neural networks · CPC title

• G06V10/764

  using classification, e.g. of video objects · CPC title

• G06F18/24137

  Distances to cluster centroïds · CPC title

• G06N3/045

  Combinations of networks · CPC title