System and method for multi-view face detection
First Claim
1. A computer-implemented face detection process for detecting a person'"'"'s face in an input image and identifying a face pose range into which the face pose exhibited by the detected face falls, comprising using a computer to perform the following process actions:
- creating a database comprising a plurality of training feature characterizations, each of which characterizes the face of a person at a known face pose or a non-face;
training a plurality of detectors arranged in a pyramidal architecture to determine whether a portion of an input image depicts a person'"'"'s face having a face pose falling within a face pose range associated with one of the detectors using the training feature characterizations; and
wherein said detectors using a greater number of feature characterizations are arranged at the bottom of the pyramid, and wherein said detectors arranged to detect finer ranges of face pose are arranged at the bottom of the pyramid;
inputting a portion of an input image into the plurality of detectors arranged in a pyramid architecture; and
interpreting the output of the plurality of detectors to determine whether the portion of the input image contains a face and if so to identify the pose associated with each detected face.
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Accused Products
Abstract
A system and method for real-time multi-view (i.e. not just frontal view) face detection. The system and method uses a sequence of detectors of increasing complexity and face/non-face discriminating thresholds to quickly discard non-faces at the earliest stage possible, thus saving much computation compared to prior art systems. The detector-pyramid architecture for multi-view face detection uses a coarse-to-fine and simple-to-complex scheme. This architecture solves the problem of lengthy processing that precludes real-time face detection effectively and efficiently by discarding most of non-face sub-windows using the simplest possible features at the earliest possible stage. This leads to the first real-time multi-view face detection system which has the accuracy almost as good as the state-of-the-art system yet 270 times faster, allowing real-time performance.
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Citations
31 Claims
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1. A computer-implemented face detection process for detecting a person'"'"'s face in an input image and identifying a face pose range into which the face pose exhibited by the detected face falls, comprising using a computer to perform the following process actions:
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creating a database comprising a plurality of training feature characterizations, each of which characterizes the face of a person at a known face pose or a non-face;
training a plurality of detectors arranged in a pyramidal architecture to determine whether a portion of an input image depicts a person'"'"'s face having a face pose falling within a face pose range associated with one of the detectors using the training feature characterizations; and
whereinsaid detectors using a greater number of feature characterizations are arranged at the bottom of the pyramid, and wherein said detectors arranged to detect finer ranges of face pose are arranged at the bottom of the pyramid;
inputting a portion of an input image into the plurality of detectors arranged in a pyramid architecture; and
interpreting the output of the plurality of detectors to determine whether the portion of the input image contains a face and if so to identify the pose associated with each detected face. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 24, 25)
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21. (canceled)
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28. (canceled)
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29. A system for detecting an object in an input image, the system comprising:
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a general purpose computing device; and
a computer program comprising program modules executable by the computing device, wherein the computing device is directed by the program modules of the computer program to create a database comprising a plurality of training feature characterizations, each of which characterizes an object being sought at a known orientation or a an object not being sought;
train a plurality of detectors arranged in a pyramidal architecture to determine whether a portion of an input image depicts an object being sought having an orientation within an orientation associated with one of the detectors using the training feature characterizations; and
whereinsaid detectors using a greater number of feature characterizations are arranged at the bottom of the pyramid, and wherein said detectors arranged to detect finer ranges of object orientation are arranged at the bottom of the pyramid;
input a portion of an input image into the plurality of detectors arranged in a pyramid architecture; and
interpret the output of the plurality of detectors to determine whether the portion of the input image contains an object being sought and if so to identify the orientation associated with each detected object being sought.
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30. A computer-readable medium having computer-executable instructions for detecting a person'"'"'s face in an input image, said computer executable instructions comprising:
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creating a database comprising a plurality of training feature characterizations, each of which characterizes the face of a person at a known face pose or a non-face;
training a plurality of detectors arranged in a pyramidal architecture to determine whether a portion of an input image depicts a person'"'"'s face having a face pose falling within a face pose range associated with one of the detectors using the training feature characterizations, said plurality of detectors when trained being capable of determining whether a portion of an input image depicts a person'"'"'s face; and
whereinsaid detectors using a greater number of feature characterizations are arranged at the bottom of the pyramid, and wherein said detectors arranged to detect finer ranges of face pose are arranged at the bottom of the pyramid.
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31. A face detection system for detecting a person'"'"'s face depicted in an input image and identifying a face pose range, among a set of pose ranges, into which the pose associated with the detected face falls, comprising:
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a database comprising a plurality of training feature characterizations, each of which characterizes the face of a person at a known face pose or a non-face;
a plurality of detectors arranged in a pyramidal architecture to determine whether a portion of an input image depicts a person'"'"'s face having a face pose falling within a face pose range associated with one of the detectors using said training feature characterizations; and
whereinsaid detectors using a greater number of feature characterizations are arranged at the bottom of the pyramid, and wherein said detectors arranged to detect finer ranges of face pose are arranged at the bottom of the pyramid.
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Specification