Dataset-aware and invariant learning for face recognition
AssigneeWyze Labs, Inc.
InventorsLin Chen, Gaoang Wang, Tianqiang Liu
Life of this patent
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Abstract
Introduced here is an approach to training a model to perform facial recognition with a designed dataset-aware loss. This approach is particularly useful for large-scale, multi-dataset training since there are no prerequisites for label cleaning. Dataset-aware loss may be built upon the softmax function with a binary dataset indicator. Such an approach allows the dataset-aware loss to be readily combined with other losses to improve performance.
Claims20
A non-transitory medium with instructions stored thereon that, when executed by a processor of a computing device, cause the computing device to perform operations comprising:
- obtaining
- (i) a first dataset that includes a first series of facial images of a first set of individuals, and
- (ii) a second dataset that includes a second series of facial images of a second set of individuals that shares at least one individual in common with the first set of individuals;
- for each facial image in the first dataset,
- computing an embedding and then determining loss for the embedding within the first dataset, so as to produce a first series of loss metrics;
- for each facial image in the second dataset,
- computing an embedding and then determining loss for the embedding within the second dataset, so as to produce a second series of loss metrics;
- and
- storing the first and second series of loss metrics in a data structure that is associated with a model to be trained to perform facial recognition using the first and second datasets.
A method comprising:
- forming, by a processor, a training set from multiple datasets of facial images;
- generating, by the processor, a feature vector for each facial image in the training set using a model,
- wherein each feature vector includes
- (i) an embedding of features of a face that are determined through analysis of a corresponding facial image,
- (ii) an identity label that specifies an identity of an individual to whom the face belongs, and
- (iii) an index label that specifies a dataset of which the corresponding facial image is a part;
- determining, by the processor, loss for each feature vector entirely within a corresponding dataset, such that loss is separately determined within each dataset of the multiple datasets;
- and
- propagating, by the processor, the losses to the model, so as to produce a trained model that is able to perform facial recognition;
- and
- storing, by the processor, the trained model in a data structure.
A system for training a model to perform facial recognition, the system comprising:
- an embedding network that takes, as input, an image of a face to be recognized and then outputs an embedding;
- a classification network that takes, as input, the embedding and then outputs a first label that is representative of a proposed identity of an individual to whom the face belongs;
- and
- a dataset classifier network that takes, as input, the embedding and then outputs a second label that is representative of a mapping of the first label to a dataset to which the image belongs.
Dependencies are inferred from the claim text.