Machines that interact with humans can do so better if they can also visually understand us, but they have limited resources to do so. The main topic of this dissertation is contrasting the use of resources by machine vision systems against the accuracy obtained by them. This thesis focuses on reducing the need for data, memory, and computation in real-world machine vision systems, applied to human observation and face analysis. This dissertation tackles annotation effort by exploring how weakly-supervised object/person detectors can be improved. Findings show that prior knowledge about objects' bounds in images helps the detector learn the spatial extent of objects using only weak image-level labels. The proposed implementation enables single-shot detection, thus improving computational efficiency of this data-efficient method. The thesis also demonstrates how prior knowledge about eye locations can be used to reduce the computational burden of gaze tracking: non-vital parts of the input image can be discarded without losing accuracy. Additionally, the thesis finds how a priori known geometrical relations can be exploited to project gaze onto a screen with little human annotation effort. Findings of this dissertation further suggest that spatial structures in images can be exploited for improving efficiency of vision tasks. The proposed solution allows for learning detection of facial occlusions and anomalies from only a few examples. Results also indicate that this solution can be used as a loss function for unsupervised pre-training of neural networks when resources are constrained. Lastly, this thesis showcases how prior know-how about blood-flow physiology in faces can be applied in a camera-based vital signs estimator. Even when data is available, this hand-crafted method performs better than deep learning methods — both in terms of accuracy and efficiency. At the same time, the results also reveal the pitfalls of assumptions made in the prior knowledge when exposed to more complex tasks — such as video compression noise filtering. Through its common theme of incorporating prior knowledge, this dissertation brings attention to the costs incurred by machine vision systems to achieve high accuracy.@en

Efficiency and ease of use are essential for practical applications of camera based eye/gaze-tracking. Gaze tracking involves estimating where a person is looking on a screen based on face images from a computer-facing camera. In this paper we investigate two complementary forms of efficiency in gaze tracking: 1. The computational efficiency of the system which is dominated by the inference speed of a CNN predicting gaze-vectors; 2. The usability efficiency which is determined by the tediousness of the mandatory calibration of the gaze-vector to a computer screen. To do so, we evaluate the computational speed/accuracy trade-off for the CNN and the calibration effort/accuracy trade-off for screen calibration. For the CNN, we evaluate the full face, two-eyes, and single eye input. For screen calibration, we measure the number of calibration points needed and evaluate three types of calibration: 1. pure geometry, 2. pure machine learning, and 3. hybrid geometric regression. Results suggest that a single eye input and geometric regression calibration achieve the best trade-off.@en

Remote photo-plethysmography (rPPG) uses a camera to estimate a person’s heart rate (HR). Similar to how heart rate can provide useful information about a person’s vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This unsupervised method requires no rPPG specific training and is able to operate in real-time. We also introduce a new multi-modal video dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR and HRV estimation. We validate and study our method under various conditions on a comprehensive range of public and self-recorded datasets, showing state-of-the-art results and providing useful insights into some unique aspects. Lastly, we make available CleanerPPG, a collection of human-verified ground truth peak/heart-beat annotations for existing rPPG datasets. These verified annotations should make future evaluations and benchmarking of rPPG algorithms more accurate, standardized and fair.@en

Remote photo-plethysmography (rPPG) uses a remotely placed camera to estimating a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rate more accurately, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This method requires no rPPG specific training and is able to operate in real-time. We validate our method on a self-recorded dataset under an idealized lab setting, and show state-of-the-art results on two public dataset with realistic conditions (VicarPPG and PURE).@en

In the face of scarcity in detailed training annotations, the ability to perform object localization tasks in real-time with weak-supervision is very valuable. However, the computational cost of generating and evaluating region proposals is heavy. We adapt the concept of Class Activation Maps (CAM) [28] into the very first weakly-supervised ‘single-shot’ detector that does not require the use of region proposals. To facilitate this, we propose a novel global pooling technique called Spatial Pyramid Averaged Max (SPAM) pooling for training this CAM-based network for object extent localisation with only weak image-level supervision. We show this global pooling layer possesses a near ideal flow of gradients for extent localization, that offers a good trade-off between the extremes of max and average pooling. Our approach only requires a single network pass and uses a fast-backprojection technique, completely omitting any region proposal steps. To the best of our knowledge, this is the first approach to do so. Due to this, we are able to perform inference in real-time at 35fps, which is an order of magnitude faster than all previous weakly supervised object localization frameworks.

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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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