Binary Decision Diagram (BDD)

Towhidi, Farnaz; Lashkari, Arashhabbibi; Hosseini, Raheleh Sadat

doi:10.1109/icfcc.2009.31

Cited by 11 publications

(1 citation statement)

References 9 publications

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“…Binary Decision Trees [38] is an acyclic graph used to represent a Boolean function. It is a data structure that consist of a decision node that labels the Boolean variable and possesses two child nodes that correspond to each variable state.…”

Section: Methodsmentioning

confidence: 99%

Automatic Prediction of Facial Trait Judgments: Appearance vs. Structural Models

et al. 2011

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Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.

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Section: Methodsmentioning

confidence: 99%