3 research outputs found
Machine Learning-based Lie Detector applied to a Novel Annotated Game Dataset
Lie detection is considered a concern for everyone in their day to day life
given its impact on human interactions. Thus, people normally pay attention to
both what their interlocutors are saying and also to their visual appearances,
including faces, to try to find any signs that indicate whether the person is
telling the truth or not. While automatic lie detection may help us to
understand this lying characteristics, current systems are still fairly
limited, partly due to lack of adequate datasets to evaluate their performance
in realistic scenarios. In this work, we have collected an annotated dataset of
facial images, comprising both 2D and 3D information of several participants
during a card game that encourages players to lie. Using our collected dataset,
We evaluated several types of machine learning-based lie detectors in terms of
their generalization, person-specific and cross-domain experiments. Our results
show that models based on deep learning achieve the best accuracy, reaching up
to 57\% for the generalization task and 63\% when dealing with a single
participant. Finally, we also highlight the limitation of the deep learning
based lie detector when dealing with cross-domain lie detection tasks
Fully end-to-end composite recurrent convolution network for deformable facial tracking in the wild
Comunicació presentada a: Proceedings of the 14th IEEE International Conference on Automatic Face and Gesture Recognition celebrat del 14 al 18 de maig a Lille, França.Human facial tracking is an important task in computer vision, which has recently lost pace compared to other facial analysis tasks. The majority of current available tracker possess two major limitations: their little use of temporal information and the widespread use of handcrafted features, without taking full advantage of the large annotated datasets that have recently become available. In this paper we present a fully end-to-end facial tracking model based on current state of the art deep model architectures that can be effectively trained from the available annotated facial landmark datasets. We build our model from the recently introduced general object tracker Re 3 , which allows modeling the short and long temporal dependency between frames by means of its internal Long Short Term Memory (LSTM) layers. Facial tracking experiments on the challenging 300-VW dataset show that our model can produce state of the art accuracy and far lower failure rates than competing approaches. We specifically compare the performance of our approach modified to work in tracking-by-detection mode and showed that, as such, it can produce results that are comparable to state of the art trackers. However, upon activation of our tracking mechanism, the results improve significantly, confirming the advantage of taking into account temporal dependencies.This work is partly supported by the Spanish Ministry of
Economy and Competitiveness under project grant TIN2017-
90124-P, the Ramon y Cajal programme, and the Maria de
Maeztu Units of Excellence Programme (MDM-2015-0502)