Methods and software for hallucinating facial features by prioritizing reconstruction errors
First Claim
1. A method of hallucinating facial features of a first face by prioritizing reconstruction errors, wherein the first face is present in an image in which a first portion of the first face is un-occluded and a second portion of the first face is occluded, the method comprising:
- receiving the image of the first face, the first portion containing one or more first facial features;
training a machine-learning algorithm using a set of images each containing a region of a face of an individual corresponding to the first portion of the first face and a region of the face of the individual corresponding to the second portion of the first face so as to produce machine-learning data or receiving machine-learning data corresponding to a previous implementation of such training; and
hallucinating one or more second facial features within the second portion of the first face as a function of the machine-learning data by prioritizing reconstruction errors for hallucinating the one or more second facial features such that reconstruction error for the one or more first facial features is minimized with a higher priority than reconstruction error for hallucinating the one or more second facial features.
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Abstract
Identifying a masked suspect is one of the toughest challenges in biometrics that exist. This is an important problem faced in many law-enforcement applications on almost a daily basis. In such situations, investigators often only have access to the periocular region of a suspect'"'"'s face and, unfortunately, conventional commercial matchers are unable to process these images in such a way that the suspect can be identified. Herein, a practical method to hallucinate a full frontal face given only a periocular region of a face is presented. This approach reconstructs the entire frontal face based on an image of an individual'"'"'s periocular region. By using an approach based on a modified sparsifying dictionary learning algorithm, faces can be effectively reconstructed more accurately than with conventional methods. Further, various methods presented herein are open set, and thus can reconstruct faces even if the algorithms are not specifically trained using those faces.
7 Citations
20 Claims
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1. A method of hallucinating facial features of a first face by prioritizing reconstruction errors, wherein the first face is present in an image in which a first portion of the first face is un-occluded and a second portion of the first face is occluded, the method comprising:
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receiving the image of the first face, the first portion containing one or more first facial features; training a machine-learning algorithm using a set of images each containing a region of a face of an individual corresponding to the first portion of the first face and a region of the face of the individual corresponding to the second portion of the first face so as to produce machine-learning data or receiving machine-learning data corresponding to a previous implementation of such training; and hallucinating one or more second facial features within the second portion of the first face as a function of the machine-learning data by prioritizing reconstruction errors for hallucinating the one or more second facial features such that reconstruction error for the one or more first facial features is minimized with a higher priority than reconstruction error for hallucinating the one or more second facial features. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A machine-readable storage medium containing machine-executable instructions for performing a method of hallucinating facial features of a first face by prioritizing reconstruction errors, wherein the first face is present in an image in which a first portion of the first face is un-occluded and a second portion of the first face is occluded, said machine-executable instructions comprising:
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a first set of machine-executable instructions for receiving the image of the first face, the first portion containing one or more first facial features; a second set of machine-executable instructions for training a machine-learning algorithm using a set of images each containing a region of a face of an individual corresponding to the first portion of the first face and a region of the face of the individual corresponding to the second portion of the first face so as to produce machine-learning data or receiving machine-learning data corresponding to a previous implementation of such training; and a third set of machine-executable instructions for hallucinating one or more second facial features with the second portion of the first face as a function of the machine-learning data by prioritizing reconstruction errors for hallucinating the one or more second facial features such that reconstruction error for the one or more first facial features is minimized with a higher priority than reconstruction error for hallucinating the one or more second facial features. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20)
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Specification