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  • Broschiertes Buch

Instead of learning the specific aging pattern for each person, it is possible to learn the common pattern of aging for more than one person at different ages. For each age, more than one facial image is used for age representation. Each person can have several face images in one age or in an age range. Therefore, this model is more flexible than AGES model, and it is much easier to collect a larger number of samples (facial images) and create a larger database. This model uses a manifold embedding technique for learning a low-dimensional aging trend for many facial images of the same age. The…mehr

Produktbeschreibung
Instead of learning the specific aging pattern for each person, it is possible to learn the common pattern of aging for more than one person at different ages. For each age, more than one facial image is used for age representation. Each person can have several face images in one age or in an age range. Therefore, this model is more flexible than AGES model, and it is much easier to collect a larger number of samples (facial images) and create a larger database. This model uses a manifold embedding technique for learning a low-dimensional aging trend for many facial images of the same age. The only requirement of this model is that the sample size for learning is large enough so that embedded manifold can be taught with statistical sufficiency. The main objective of this book is to estimate age from different facial features. - To detect face parts like eyes, nose, mouth, chin etc.- To extract facial features of the input face image.- To estimate age by using the extracted features of face.
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Autorenporträt
Karande Kailash Jagannath has completed his PhD in Electronics and Telecommunication from SRTM University. He has total 100+ Publications in International and National Journals and Conferences. He has 6 books in his credit with International publications like Springer. Currently he is working as Principal at SKN Sinhgad College of Engineering.