Ground truth annotation of the occurrence and intensity of FACS Action Unit (AU) activation requires great amount of attention. The efforts towards achieving a common platform for AU evaluation have been addressed in the FG 2015 Facial Expression Recognition and Analysis challenge (FERA 2015). Participants are invited to estimate AU occurrence and intensity on a common benchmark dataset. Conventional approaches towards achieving automated methods are to train multiclass classifiers or to use regression models. In this paper, we propose a novel application of a deep convolutional neural network (CNN) to recognize AUs as part of FERA 2015 challenge. The 7 layer network is composed of 3 convolutional layers and a max-pooling layer. The final fully connected layers provide the classification output. For the selected tasks of the challenge, we have trained two different networks for the two different datasets, where one focuses on the AU occurrences and the other on both occurrences and intensities of the AUs. The occurrence and intensity of AU activation are estimated using specific neuron activations of the output layer. This way, we are able to create a single network architecture that could simultaneously be trained to produce binary and continuous classification output.

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This demonstration paper presents the FaceReader framework where human face image and skin color variations are analyzed for observing facial expressions, vital signs including but not limited to average heart rate (HR), heart rate variability (HRV) and also the stress and confidence levels of the person. Remote monitoring of the facial and vital signs could be useful for wide range of applications. FaceReader uses active appearance modeling for facial analysis and novel signal processing techniques for heart rate and variability estimation. The performance has been objectively evaluated and psychological guidelines for stress measurements are in- corporated in the framework for analysis.@en

This paper proposes a remote photoplethysmography measurement technique where human skin color variations are analysed for observing human vital signs including but not limited to average heart rate and variation. Remote monitoring of the vital signs could be useful for non-contact physiological and psychological diagnosis. For this purpose, an off-the-self non-invasive video camera is used. Facial appearance modelling is performed for stabilizing color variations in the selected facial region during the signal acquisition stage. The proposed method offers a novel signal processing approach for extracting the periodic component of the raw color signal for the heart rate and variation estimation. To this end, we have collected a ground truth dataset using a PPG instrument attached to the skin of the subject under observation. Objective performance tests show strong correlation with the ground truth values for the estimated heart rate and variation.

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