Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images

In this paper, we design and evaluate a convolutional autoencoder that perturbs an input face image to impart privacy to a subject. Specifically, the proposed autoencoder transforms an input face image such that the transformed image can be successfully used for face recognition but not for gender classification. In order to train this autoencoder, we propose a novel training scheme, referred to as semi-adversarial training in this work. The training is facilitated by attaching a semi-adversarial module consisting of a pseudo gender classifier and a pseudo face matcher to the autoencoder. The objective function utilized for training this network has three terms: one to ensure that the perturbed image is a realistic face image; another to ensure that the gender attributes of the face are confounded; and a third to ensure that biometric recognition performance due to the perturbed image is not impacted. Extensive experiments confirm the efficacy of the proposed architecture in extending gender privacy to face images

Discontinuous Element Insertion Algorithm

An algorithm is presented for inserting zero-thickness interface elements, termed herein as “couplers”, into continuous finite element meshes in two and three dimensions. Insertion is governed solely by the mesh topology and is specified according to regions or subdomains within the overall analysis domain, a geometrically intuitive means to designate the coupler locations. The algorithm is self-contained and requires only nodal coordinates and element connectivity as input. A wide class of volume elements and interface couplers are treated within the framework. Since the algorithm is topologically-based, interfaces of arbitrary complexity are naturally accommodated. Separate treatment is given to inserting couplers within regions and along region boundaries to improve efficiency. Numerical tests verify that the algorithm is computationally scalable and produces analysis suitable meshes

Low cost gaze estimation: knowledge-based solutions

Eye tracking technology in low resolution scenarios is not a completely solved issue to date. The possibility of using eye tracking in a mobile gadget is a challenging objective that would permit to spread this technology to non-explored fields. In this paper, a knowledge based approach is presented to solve gaze estimation in low resolution settings. The understanding of the high resolution paradigm permits to propose alternative models to solve gaze estimation. In this manner, three models are presented: a geometrical model, an interpolation model and a compound model, as solutions for gaze estimation for remote low resolution systems. Since this work considers head position essential to improve gaze accuracy, a method for head pose estimation is also proposed. The methods are validated in an optimal framework, I2Head database, which combines head and gaze data. The experimental validation of the models demonstrates their sensitivity to image processing inaccuracies, critical in the case of the geometrical model. Static and extreme movement scenarios are analyzed showing the higher robustness of compound and geometrical models in the presence of user's displacement. Accuracy values of about 3° have been obtained, increasing to values close to 5° in extreme displacement settings, results fully comparable with the state-of-the-art

EVALUATING A MARKERLESS METHOD FOR STUDYING ARTICULATORY MOVEMENTS: APPLICATION TO A SYLLABLE REPETITION TASK

none4siThe analysis of the articulatory movements allows investigating the kinematic characteristics of some speech disorders. However, the methodologies most used until now, as electromagnetic articulography and optoelectronic systems, are expensive and intrusive which limit their use to specialized laboratories. In this work, we use a completely markerless and low-cost technique to study lip movements during a syllable repetition task. By means of a Kinect-like and an existing face tracking algorithm, we are able to track the movements of the lower lip, testing the performances against a reference method (marker-based optoelectronic system). Good results were obtained in terms of RMSE for the tracking of the lower lip during the repetitions. Some kinematic measures, as opening and closing velocities and accelerations, were also computed. Despite the limitations in terms of image resolution, these results are very promising in the optic of developing a new markerless system for studying speech articulation.noneBandini A.; Ouni S.; Orlandi S.; Manfredi C.Bandini A.; Ouni S.; Orlandi S.; Manfredi C