Classroom Roll-Call System Based on ResNet Networks
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초록
A convolution neural networks (CNNs) has demonstrated outstanding performance compared to otheralgorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers haveproposed a residual network to ease the training for recognition accuracy improvement. In this study, a novelface recognition model based on game theory for call-over in the classroom was proposed. In the proposedscheme, an image with multiple faces was used as input, and the residual network identified each face with aconfidence score to form a list of student identities. Face tracking of the same identity or low confidence weredetermined to be the optimisation objective, with the game participants set formed from the student identitylist. Game theory optimises the authentication strategy according to the confidence value and identity set toimprove recognition accuracy. We observed that there exists an optimal mapping relation between face andidentity to avoid multiple faces associated with one identity in the proposed scheme and that the proposedgame-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.
Abstract
A convolution neural networks (CNNs) has demonstrated outstanding performance compared to otheralgorithms in the field of face recognition. Regarding the over-fitting problem of CNN, researchers haveproposed a residual network to ease the training for recognition accuracy improvement. In this study, a novelface recognition model based on game theory for call-over in the classroom was proposed. In the proposedscheme, an image with multiple faces was used as input, and the residual network identified each face with aconfidence score to form a list of student identities. Face tracking of the same identity or low confidence weredetermined to be the optimisation objective, with the game participants set formed from the student identitylist. Game theory optimises the authentication strategy according to the confidence value and identity set toimprove recognition accuracy. We observed that there exists an optimal mapping relation between face andidentity to avoid multiple faces associated with one identity in the proposed scheme and that the proposedgame-based scheme can reduce the error rate, as compared to the existing schemes with deeper neural network.
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