Anatomy based Features for Facial Expression Recognition

22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEYWOS: 000356351400023In this study we propose a set of anatomy based features for facial expression recognition. The muscle forces that constitute an expression are solved by tracking carefully selected facial feature points. These points are initialized in the muscular regions of influence on the first frame of the video. They are tracked using the optical flow algorithm. The displacements of facial feature points are used for estimation of 3 dimensional head orientation and deformations due to expressions. We model human face with springs as an over-determined and linear system of equations. This system is solved under the constraint of facial anatomy for muscular activities. We use sequential forward selection to determine the most descriptive set of features for classification of basic expressions.IEEE, Karadeniz Tech Univ, Dept Comp Engn & Elect & Elect Eng

Real-Time Defect Detection in Power Conduction Devices for Rail Transportation

22nd IEEE Signal Processing and Communications Applications Conference (SIU) -- APR 23-25, 2014 -- Karadeniz Teknik Univ, Trabzon, TURKEYWOS: 000356351400175System integrity is critical for completion of journey in rail transport systems. System integrity is dependent on the catenary line, pantograph and the train tracks in addition to the locomotive and the wagons. Catenary line and pantograph are the main devices for transmission of electricity to the locomotive. The level of tracks has a direct influence on the continuity of conduction between the catenary line and the pantograph. Settling of tracks momentarily separates the conduction devices, resulting in arcs. In this study we aim to detect catenary line, pantograph and track related problems automatically and in real time for safety of railroad transportation.IEEE, Karadeniz Tech Univ, Dept Comp Engn & Elect & Elect Eng

Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness

In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax function on the logits to get the probability scores of each class. In this type of architectures, the loss value of the classifier against any output class is directly proportional to the difference between the final probability score and the label value of the associated class. Standard White-box adversarial evasion attacks, whether targeted or untargeted, mainly try to exploit the gradient of the model loss function to craft adversarial samples and fool the model. In this study, we show both mathematically and experimentally that using some widely known activation functions in the output layer of the model with high temperature values has the effect of zeroing out the gradients for both targeted and untargeted attack cases, preventing attackers from exploiting the model's loss function to craft adversarial samples. We've experimentally verified the efficacy of our approach on MNIST (Digit), CIFAR10 datasets. Detailed experiments confirmed that our approach substantially improves robustness against gradient-based targeted and untargeted attack threats. And, we showed that the increased non-linearity at the output layer has some additional benefits against some other attack methods like Deepfool attack.Comment: IEEE COMPSAC 2022 publication full versio

The routine design-modular distributed modeling platform for distributed routine design and simulation-based testing of distributed assemblies

In this paper we describe a conceptual framework and implementation of a tool that supports task-directed, distributed routine design (RD) augmented with simulation-based design testing. In our research, we leverage the modular distributed modeling (MDM) methodology to simulate the interaction of design components in an assembly. The major improvement we have made in the RD methodology is to extend it with the capabilities of incorporating remotely represented off-the-shelf components in design and simulation-based testing of a distributed assembly. The deliverable of our research is the RD-MDM platform, which is capable of automatically selecting intellectually protected off the shelf design components over the Internet, integrating these components in an assembly, running simulations for design testing, and publishing the approved design without disclosing the proprietary information.Publisher's Versio