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Cybersecurity / Biometrics: A Machine Learning Approach to Automated Face Recognition and Identification - Softcover
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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.
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Über die Autorin bzw. den Autor
Dr. Emmanuel Ogala holds a Bachelor of Science (B.Sc.), Master of Technology (M.Tech.), and Doctor of Philosophy (Ph.D.) in Computer Science. He is an accomplished academic and researcher with extensive experience in teaching, research, and innovation in computing technologies. His research interests include computer science applications.
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Cybersecurity / Biometrics
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Cybersecurity / Biometrics : A Machine Learning Approach to Automated Face Recognition and Identification
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786630194357
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Cybersecurity / Biometrics
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Taschenbuch. Zustand: 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. Bestandsnummer des Verkäufers 9786630194357
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Cybersecurity / Biometrics | A Machine Learning Approach to Automated Face Recognition and Identification
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Taschenbuch. Zustand: Neu. Cybersecurity / Biometrics | A Machine Learning Approach to Automated Face Recognition and Identification | Emmanuel Ogala | Taschenbuch | Englisch | 2026 | GlobeEdit | EAN 9786630194357 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Bestandsnummer des Verkäufers 135985113
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