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• W2908331575 abstract "Face recognition has prodigious success in biometric and surveillance sectors. The key drawback in face recognition is dealing with different poses i.e., recognition of faces in arbitrary in-depth rotations (which depends upon camera rotation/person face movement). This paper deals with aligning the pose variation taken in non-overlapping camera views and the recognition is attained using this aligned face. Face fitting is done by Relevance Metric Learning with List-wise Constraints (RMLLC). Subsequently, Constrained Local Models (CLMs) framework otherwise Discriminative Response Map Fitting (DRMF) method, is adopted for fitting. Afterwards, the adjacent faces are aligned by means of transformation. Finally, all pairwise similarities for query frame against database frames is used to recognize face. The proposed work exemplifies substantial improvement which is validated by matching rate. The results obtained using CVL database shows the efficacy of the proposed algorithm under different pose variations." @default.
• W2908331575 created "2019-01-11" @default.
• W2908331575 creator A5048540225 @default.
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• W2908331575 creator A5090517143 @default.
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• W2908331575 date "2017-12-01" @default.
• W2908331575 modified "2023-09-25" @default.
• W2908331575 title "Face recognition across pose for PTZ camera video surveillance applications" @default.
• W2908331575 cites W1605761733 @default.
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• W2908331575 doi "https://doi.org/10.1109/icapr.2017.8593006" @default.
• W2908331575 hasPublicationYear "2017" @default.
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