DEEP MULTI-TASK LEARNING FRAMEWORK FOR FACE DETECTION, LANDMARK LOCALIZATION, POSE ESTIMATION, AND GENDER RECOGNITION
First Claim
1. An apparatus, comprising:
- a first module of at least three modules, wherein the first module configured to generate class independent region proposals to provide a region;
a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis; and
a third module of the at least three modules is configured to perform post-processing on the classified region.
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Abstract
Various image processing may benefit from the application deep convolutional neural networks. For example, a deep multi-task learning framework may assist face detection, for example when combined with landmark localization, pose estimation, and gender recognition. An apparatus can include a first module of at least three modules configured to generate class independent region proposals to provide a region. The apparatus can also include a second module of the at least three modules configured to classify the region as face or non-face using a multi-task analysis. The apparatus can further include a third module configured to perform post-processing on the classified region.
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Citations
20 Claims
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1. An apparatus, comprising:
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a first module of at least three modules, wherein the first module configured to generate class independent region proposals to provide a region; a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis; and a third module of the at least three modules is configured to perform post-processing on the classified region. - View Dependent Claims (2, 3, 4, 5, 15)
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6. An apparatus, comprising:
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at least one processor; and at least one memory including computer program instructions, wherein the at least one memory and the computer program instructions are configured to select a set of data for facial analysis; and apply the set of data to a network comprising at least three modules, wherein a first module of the at least three modules is configured to generate class independent region proposals to provide a region, wherein a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis, wherein a third module of the at least three modules is configured to perform post-processing on the classified region. - View Dependent Claims (7, 8, 9, 10)
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11. A method, comprising:
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selecting a set of data for facial analysis; and applying the set of data to a network comprising at least three modules, wherein a first module of the at least three modules is configured to generate class independent region proposals to provide a region, wherein a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis, wherein a third module of the at least three modules is configured to perform post-processing on the classified region. - View Dependent Claims (12, 13, 14)
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16. An apparatus, comprising:
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means for selecting a set of data for facial analysis; and means for applying the set of data to a network comprising at least three modules, wherein a first module of the at least three modules is configured to generate class independent region proposals to provide a region, wherein a second module of the at least three modules is configured to classify the region as face or non-face using a multi-task analysis, wherein a third module of the at least three modules is configured to perform post-processing on the classified region. - View Dependent Claims (17, 18, 19, 20)
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Specification