Abstract
In this paper, we suggested AdBoost algorithm for further improvising the performance of system. In the enhanced adaboost, the eigen vectors are computed for facial region & applied classification. In the process of classification, we opt for process of learning, training & testing. As observed from the result sessions in the previous paper [13] the outcomes from the reboost detection are observed. They provide the results in the form of Detection rate & false alarm rate. We applied the suggested methodology & improvising the performance of false alarm rate & detection rate. We have raised the rate of detection & false alarm rate. For presenting the contrasts, we have designed GUI window where comparisons are presented.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
