Shanto Rahman, Lecturer at Information and Communication Technology (ICT), Bangladesh University of Professionals. She pursued her M.Sc and B.Sc from university of Dhaka. As a student, her tremendous efforts have earned lots of accomplishment by creating numerous real life software application which are also being used from several stakeholders as well as her publication in various SCI indexed journals. Her sound programming skills bolster her to get appointment from Samsung R&D Institute Bangladesh as a senior software engineer at the prelude of her venture in the sector of job. Her core areas of interest are Computer Vision, Software Engineering and Artificial Intelligence. She is the coach of several national and international programming contest such as ICPC, NCPC, etc. Currently she is supervising many of the students as a researchers.
Gender recognition from facial images has become an empirical aspect in present world. It is one ... more Gender recognition from facial images has become an empirical aspect in present world. It is one of the main problems of computer vision and researches have been conducting on it. Though several techniques have been proposed, most of the techniques focused on facial images in controlled situation. But the problem arises when the classification is performed in uncontrolled conditions like high rate of noise, lack of illumination, etc. To overcome these problems, we propose a new gender recognition framework which first preprocess and enhances the input images using Adaptive Gama Correction with Weighting Distribution. We used Labeled Faces in the Wild (LFW) database for our experimental purpose which contains real life images of uncontrolled condition. For measuring the performance of our proposed method, we have used confusion matrix, precision, recall, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR). In every case, our proposed framework performs superior over other existing state-of-the-art techniques.
Gender recognition from facial images has become an empirical aspect in present world. It is one ... more Gender recognition from facial images has become an empirical aspect in present world. It is one of the main problems of computer vision and researches have been conducting on it. Though several techniques have been proposed, most of the techniques focused on facial images in controlled situation. But the problem arises when the classification is performed in uncontrolled conditions like high rate of noise, lack of illumination, etc. To overcome these problems, we propose a new gender recognition framework which first preprocess and enhances the input images using Adaptive Gama Correction with Weighting Distribution. We used Labeled Faces in the Wild (LFW) database for our experimental purpose which contains real life images of uncontrolled condition. For measuring the performance of our proposed method, we have used confusion matrix, precision, recall, F-measure, True Positive Rate (TPR), and False Positive Rate (FPR). In every case, our proposed framework performs superior over other existing state-of-the-art techniques.
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Papers by Shanto Rahman