Face detection using division-generated Haar-like features for illumination invariance
First Claim
1. A method for determining whether a portion of an image contains a face, comprising:
- for each region of an N number of regions of an image, determining a region value for that region such that an N number of region values are derived, wherein the N regions are within a particular portion of the image, and wherein N is an integer greater than two;
generating a first feature value for a first feature, wherein generating the first feature value comprises;
determining a first ratio by dividing a first sum of a first subset of the N number of region values by a second sum of a second subset of the N number of region values, wherein the first subset of the N number of region values corresponds to different regions than the second subset of the N number of region values; and
determining the first feature value based on the first ratio;
obtaining a first value range that is associated with the first feature, wherein first feature values within the first value range indicate a likelihood that a face is present;
determining whether the first feature value falls within the first value range; and
in response to determining that the first feature value does not fall within the first value range, storing data that indicates that the particular portion of the image does not contain a face;
wherein the method is performed by one or more computing devices.
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Abstract
Faces in images are quickly detected with minimal memory resource usage. Instead of calculating a Haar-like feature value by subtracting the average pixel intensity value in one rectangular region from the average pixel intensity value in another, adjacent rectangular region, a face-detection system calculates that Haar-like feature value by dividing the average pixel intensity value in one rectangular region by the average pixel intensity value in another adjacent rectangular region. Thus, each Haar-like value is calculated as a ratio of average pixel intensity values rather than as a difference between such average pixel intensity values. The feature values are calculated using this ratio-based technique both during the machine-learning procedure, in which the numerical ranges for features in known face-containing images are learned based on labeled training data, and during the classifier-applying procedure, in which an unlabeled image'"'"'s feature values are calculated and compared to the previously machine-learned numerical ranges.
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Citations
24 Claims
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1. A method for determining whether a portion of an image contains a face, comprising:
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for each region of an N number of regions of an image, determining a region value for that region such that an N number of region values are derived, wherein the N regions are within a particular portion of the image, and wherein N is an integer greater than two; generating a first feature value for a first feature, wherein generating the first feature value comprises; determining a first ratio by dividing a first sum of a first subset of the N number of region values by a second sum of a second subset of the N number of region values, wherein the first subset of the N number of region values corresponds to different regions than the second subset of the N number of region values; and determining the first feature value based on the first ratio; obtaining a first value range that is associated with the first feature, wherein first feature values within the first value range indicate a likelihood that a face is present; determining whether the first feature value falls within the first value range; and in response to determining that the first feature value does not fall within the first value range, storing data that indicates that the particular portion of the image does not contain a face; wherein the method is performed by one or more computing devices. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A non-transitory computer readable storage medium storing instructions which, when executed by one or more processors, cause the one or more processors to perform operations comprising:
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for each region of an N number of regions of an image, determining a region value for that region such that an N number of region values are derived, wherein the N regions are within a particular portion of the image, and wherein N is an integer greater than two; generating a first feature value for a first feature, wherein generating the first feature value comprises; determining a first ratio by dividing a first sum of a first subset of the N number of region values by a second sum of a second subset of the N number of region values, wherein the first subset of the N number of region values corresponds to different regions than the second subset of the N number of region values; and determining the first feature value based on the first ratio; obtaining a first value range that is associated with the first feature, wherein first feature values within the first value range indicate a likelihood that a face is present; determining whether the first feature value falls within the first value range; and in response to determining that the first feature value does not fall within the first value range, storing data that indicates that the particular portion of the image does not contain a face. - View Dependent Claims (10, 11, 12, 13, 14, 15, 16)
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17. A device comprising:
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one more storages capable of storing one or more images and other data; and a face detector configured to perform operations comprising; for each region of an N number of regions of an image, determining a region value for that region such that an N number of region values are derived, wherein the N regions are within a particular portion of the image, and wherein N is an integer greater than two; generating a first feature value for a first feature, wherein generating the first feature value comprises; determining a first ratio by dividing a first sum of a first subset of the N number of region values by a second sum of a second subset of the N number of region values, wherein the first subset of the N number of region values corresponds to different regions than the second subset of the N number of region values; determining the first feature value based on the first ratio; obtaining a first value range that is associated with the first feature, wherein first feature values within the first value range indicate a likelihood that a face is present; determining whether the first feature value falls within the first value range; and in response to determining that the first feature value does not fall within the first value range, storing data that indicates that the particular portion of the image does not contain a face. - View Dependent Claims (18, 19, 20, 21, 22, 23, 24)
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Specification