Chi Nhan Duong

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The “interpretation through synthesis”, i.e. Active Appearance Models (AAMs) method, has received considerable attention over the past decades. It aims at “explaining” face images by synthesizing them via a parameterized model of appearance. It is quite challenging due to appearance variations of human face images, e.g. facial(More)
—The " interpretation through synthesis " approach to analyze face images, particularly Active Appearance Models (AAMs) method, has become one of the most successful face modeling approaches over the last two decades. AAM models have ability to represent face images through synthesis using a controllable parameterized Principal Component Analysis (PCA)(More)
Modeling the face aging process is a challenging task due to large and non-linear variations present in different stages of face development. This paper presents a deep model approach for face age progression that can efficiently capture the non-linear aging process and automatically synthesize a series of age-progressed faces in various age ranges. In this(More)
There has been considerable research in the last several years based on the principles of Active Appearance Models (AAMs). AAM is a robust methodology for general image (object) descriptions that incorporates shape and texture information. In this work, we extend the basic AAMs by developing a new method for texture description for the application of human(More)
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