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.
| Autorius: | Kailash Karande, Ashwini Ingole, Rajashree Badage, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2020 |
| Knygos puslapių skaičius: | 68 |
| ISBN-10: | 6202554126 |
| ISBN-13: | 9786202554121 |
| Formatas: | 220 x 150 x 5 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
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