Physiognomy in New Era: A Survey of Automatic Personality Prediction Based on Facial Image
17 Nov 2018-pp 12-29
TL;DR: A survey of automatic personality prediction based on face is presented in this paper, which provides a solid knowledge base about the state-of-the-art in automatic personality predictions, and also provides a conceptual model based on the literature.
Abstract: At present, personality computing technology facilitates the understanding, prediction, and management of human behavior. With the increasing importance of faces in personal daily assessments, establishing a relationship between facial morphological features and personality traits is a major breakthrough in personality computing technology. This paper is a survey of such technology of automatic personality prediction based on face and it aims at providing not only a solid knowledge base about the state-of-the-art in automatic personality prediction, but also to provide a conceptual model of automatic personality prediction, based on the literature. In addition, the analysis of the prediction results of the existing researches is emphasized, and there are still problems in the field, such as lack of information on research data, single age group of the sample population, incomplete design characteristics of the artificial design etc., and the potential applications and development directions are determined.
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Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
Proceedings Article•
03 Dec 2012TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
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TL;DR: A near-real-time computer system that can locate and track a subject's head, and then recognize the person by comparing characteristics of the face to those of known individuals, and that is easy to implement using a neural network architecture.
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10,114 citations
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4,453 citations