Bi-modal First Impressions Recognition Using Temporally Ordered Deep Audio and Stochastic Visual Features

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• 关键词：Deep learning ; Bi ; modal neural networks ; First impressions analysis ; Apparent personality analysis
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9915
• 期：1
• 页码：337-348
• 丛书名：Computer Vision ?ECCV 2016 Workshops
• ISBN：978-3-319-49409-8
• 卷排序：9915

文摘

We propose a novel approach for First Impressions Recognition in terms of the Big Five personality-traits from short videos. The Big Five personality traits is a model to describe human personality using five broad categories: Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness. We train two bi-modal end-to-end deep neural network architectures using temporally ordered audio and novel stochastic visual features from few frames, without over-fitting. We empirically show that the trained models perform exceptionally well, even after training from a small sub-portions of inputs. Our method is evaluated in ChaLearn LAP 2016 Apparent Personality Analysis (APA) competition using ChaLearn LAP APA2016 dataset and achieved excellent performance.