Deep Learning Approaches to Cloud Security. Группа авторов

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СКАЧАТЬ the end, similarity detection is achieved by using the Nearest Neighbor Approach where the classifiers can be trained using a suitable algorithm like K-Nearest Neighbor.

       STEPS:

      1 1. An image captured is used as input.

      2 2. Detect the image using fiducially points.

      3 3. Transform the image by cropping the subsequent markers.

      4 4. Apply Feature Selection and Feature Extraction for improved accuracy.

      5 5. Use the Feature Vector Extraction method requiring features offloaded from the criminal database on the cloud.

      6 6. Interpret and evaluate by comparing the two data sets.

      7 7. Use the Nearest Neighbor Approach to identify the similarities between two or more images.

Facial Recognition Technology is intended to operate at a distance, without the knowledge of the target, so that it becomes hard to prevent the face from being captured. Along these lines, it allows for targeting of multiple persons one after another. Moreover, it is a non-consensual and clandestine reconnaissance innovation. The proposed model, when brought to realization, could act as an effective tool for criminal identification by comparing a live capture or digital image to the stored face print in order to confirm an individual’s identity.

1.6 Future Scope

      Biometrics pose danger to individual rights and privacy since technologies like facial recognition allow identification of citizens without their acknowledgement. Moreover, when consent is backed into the design of the technology, the privacy concerns regarding biometrics could be addressed [16].

      Any modern technology is laden with concealed threats with no claim of infallibility either by the software maker, person selling it, or the one who advocates its deployment. In the context of criminal justice administration research, it indicates that images captured with default camera settings preferably expose fair complexion rather than dark, affecting results of Facial Recognition Technology across racial groups. One methodology might be to utilize a technology-neutral regulatory framework that identifies degrees of damages.

1.7 Conclusion

      Biometric technologies have wide-ranging applications. They are being increasingly used every day for phone security, banks, and governments looking towards these technologies as security measures for verifying transactions. Important government organizations are using facial recognition technology to create databases using driver’s license and passport details for effective administration, socio-economic development, and law enforcement. The thought that refined innovation implies more prominent proficiency should be fundamentally dissected. As these technologies penetrate more and more into our everyday lives, it is imperative to know and be educated about them. A reasonable strategy with ample safeguards for data protection and privacy is the need of the hour.

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