9786200230768 - Gender recognition using facial images von Fatima, Rubia; Yasin, Affan; Basit Dogar, Abdul (6 Ergebnisse)

Paperback. Zustand: Brand New. 96 pages. 8.66x5.91x0.22 inches. In Stock.

Taschenbuch. Zustand: Neu. Gender recognition using facial images | Rubia Fatima (u. a.) | Taschenbuch | 96 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200230768 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm. 96 pp. Englisch.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Fatima RubiaRubia Fatima received her Master s degree in Information Technology (IT) from Bahauddin Zakariya University (B.Z.U), Multan, Pakistan in 2016. Currently, she is pursuing her Ph.D. in Software Engineering from School of So.

Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 96 pp. Englisch.

Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The main objective of this study is to find out which of the most-widely used machine learning algorithms perform well for gender recognition. The aim of the study is to develop a system that can recognize the gender of a human on the basis of frontal facial features only. This system will classify the unknown facial images into male or female by comparing it with the images in the training set. The comparison will be done between most commonly used techniques for gender recognition that are the Genetic Algorithm (GA) and Support Vector Machine (SVM ) based on the facial features of a static image. Our results showed that our proposed SVM is better in detecting gender as compared to Genetic Algorithm.