Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Face recognition has emerged as one of the most significant biometric technologies for automated identification and authentication in modern security, surveillance, and access control systems. Traditional identification methods, such as passwords and identity cards, are increasingly vulnerable to theft, forgery and unauthorized access, thereby necessitating the development of intelligent and reliable biometric solutions. This study presents a machine learning approach to automated face recognition and identification aimed at enhancing the accuracy, efficiency, and robustness of human identity verification systems. The proposed framework employs advanced image preprocessing techniques, including face detection, normalization, feature extraction, and dimensionality reduction to improve the quality and discriminative power of facial data. Machine learning algorithms are utilized to learn distinctive facial features from a large dataset of facial images and subsequently classify individuals based on their unique biometric characteristics. The system is trained and evaluated using benchmark facial image datasets under varying conditions such as illumination changes, facial expressions. N� de r�f. du vendeur 9786630194357

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Cybersecurity / Biometrics

Emmanuel Ogala

Edit� par Globeedit Jun 2026, 2026

ISBN 10 : 6630194357 ISBN 13 : 9786630194357

Neuf Taschenbuch

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Face recognition has emerged as one of the most significant biometric technologies for automated identification and authentication in modern security, surveillance, and access control systems. Traditional identification methods, such as passwords and identity cards, are increasingly vulnerable to theft, forgery and unauthorized access, thereby necessitating the development of intelligent and reliable biometric solutions. This study presents a machine learning approach to automated face recognition and identification aimed at enhancing the accuracy, efficiency, and robustness of human identity verification systems. The proposed framework employs advanced image preprocessing techniques, including face detection, normalization, feature extraction, and dimensionality reduction to improve the quality and discriminative power of facial data. Machine learning algorithms are utilized to learn distinctive facial features from a large dataset of facial images and subsequently classify individuals based on their unique biometric characteristics. The system is trained and evaluated using benchmark facial image datasets under varying conditions such as illumination changes, facial expressions. 80 pp. Englisch. N� de r�f. du vendeur 9786630194357

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