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An efficient deep learning framework for occlusion face prediction system

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Abstract

Generally, face detection or prediction and tracking technology is the most critical research direction for target tracking and identifying criminal activities. However, crime detection in a surveillance system is complex to use. Moreover, preprocessing layer takes more time and needs to get pure-quality data. This research designed a novel, Crow Search-based Recurrent Neural Scheme to enhance the prediction performance of occlusion faces and improve classification results. Thus, the developed model was implemented in the Python tool, and the online COFW dataset was collected and trained for the system. Furthermore, enhance the performance of prediction accuracy and classify the person accurately by using Crow search fitness. Thus, the designed optimization technique tracks and searches the person's location and predicts the occlusion faces using labels. Finally, developed model experimental outcomes show better performance in predicting the occlusion faces, and the attained results are validated with prevailing models. The designed model gained 98.75% accuracy, 99% recall, and 98.56% precision for predicting occlusion faces. It shows the efficiency of the developed model and attains better performance while comparing other models.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Annamalai University, Annamalainagar, Tamilnadu, India

    S. Naveen Kumar Polisetty & T. Sivaprakasam

  2. Department of CSE-IOT, CS St. Ann’s College of Engineering & Technology, Chirala, Andhra Pradesh, India

    Indraneel Sreeram

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  1. S. Naveen Kumar Polisetty
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  2. T. Sivaprakasam
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  3. Indraneel Sreeram
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SNKP, TS, IS discussed and constructed the measures, found their applications, and wrote the paper together.

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Correspondence to S. Naveen Kumar Polisetty.

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Naveen Kumar Polisetty, S., Sivaprakasam, T. & Sreeram, I. An efficient deep learning framework for occlusion face prediction system. Knowl Inf Syst 65, 5043–5063 (2023). https://doi.org/10.1007/s10115-023-01896-5

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  • DOI: https://doi.org/10.1007/s10115-023-01896-5

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