Anthropometry of the head and face

By C. Cacou. 1995

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks (2016, methodology): “We follow the taxonomy from Guo (2012) and mention: wrinkle models (Kwon and Vitoria 1999), anthropometric models (Farkas and Schendel 1995; Kwon and Vitoria 1999; Ramanathan and Chellappa 2006), active appearance models (AAM) (Cootes et al.”
Active and dynamic information fusion for facial expression understanding from image sequences (2005, background): “Given the detected eye positions, the initial regions of these features are located using some anthropometric statistics [44] that characterize the spatial relationships between eyes and nose and between nose and mouth corners, etc.”
What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics (2016, methodology): “correlated (Farkas [138]); thus, the model used for assessing”
Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach (2007, methodology): “Based on Farkas' work [18], we ensured that the model is anthropometrically correct and it was annotated into different facial areas, as depicted in Fig.”
Modeling Age Progression in Young Faces (2006, methodology): “Farkas [7] provides a comprehensive overview of face anthropometry and its many significant applications.”
How We've Taught Algorithms to See Identity: Constructing Race and Gender in Image Databases for Facial Analysis (2020, methodology): “We incorporated multiple facial coding schemes aimed at capturing facial morphology using craniofacial features [45, 46, 135].”
Age Synthesis and Estimation via Faces: A Survey (2010, methodology): “Finally, the anthropometric model-based representations only consider the facial geometry but not any texture information, and the anthropometric measurements from Farkas [54] are mainly for European-descent faces rather than for people from other origins.”
Age-Invariant Face Recognition (2010, methodology): “To incorporate the growth pattern of the cranium for ages under 19, we rescale the overall size of 3D shapes according to the average anthropometric head width found in [22].”
Anthropometric Facial Analysis of the African American Woman (2001, unknown): “Computer analysis of the photographs was performed, and the results were compared with those of Farkas.(1) They determined that African American and white subjects had similar vertical facial proportions; however, the horizontal proportions varied significantly.”
Computer Vision and Image Understanding (2016, methodology): “With the notation used by Farkas notation ( Farkas, 1994 ), the landmarks which deﬁne the two geodesic paths a and b are: It cannot be assumed that a geodesic path joining landmarks 2 and 14 always goes through the same surface for any real face, e.g. through the neck.”
A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition (2006, methodology): “Landmark points on the 3D face are selected based on descriptions by Farkas [18].”
International Anthropometric Study of Facial Morphology in Various Ethnic Groups/Races (2005, methodology): “For evaluation of the distributional differences among measurement values in ethnic groups (Caucasians of Europe, ethnic groups in the Middle East, Asia and Africa), the data base of the North American white population was used as a reference group.(13)”