Vojtech Franc

Multi-class support vector machine

We propose a transformation from the multi-class support vector machine (SVM) classification problem to the single-class SVM problem which is more convenient for optimization. The proposed transformation is based on simplifying the original problem and employing the Kesler construction which can be carried out by the use of properly defined kernel only. The experiments conducted indicate that the proposed method is comparable with the one-against-all decomposition solved by the state-of-the-art sequential minimal optimizer algorithm.

Face and landmark detection by using cascade of classifiers

In this paper, we consider face detection along with facial landmark localization inspired by the recent studies showing that incorporating object parts improves the detection accuracy. To this end, we train roots and parts detectors where the roots detector returns candidate image regions that cover the entire face, and the parts detector searches for the landmark locations within the candidate region. We use a cascade of binary and one-class type classifiers for the roots detection and SVM like learning algorithm for the parts detection. Our proposed face detector outperforms the most of the successful face detection algorithms in the literature and gives the second best result on all tested challenging face detection databases. Experimental results show that including parts improves the detection performance when face images are large and the details of eyes and mouth are clearly visible, but does not introduce any improvement when the images are small.

Real-time multi-view facial landmark detector learned by the structured output SVM

While the problem of facial landmark detection is getting big attention in the computer vision community recently, most of the methods deal only with near-frontal views and there is only a few really multi-view detectors available, that are capable of detection in a wide range of yaw angle (e.g. Φ ε (-90°, 90°)). We describe a multi-view facial landmark detector based on the Deformable Part Models, which treats the problem of the simultaneous landmark detection and the viewing angle estimation within a structured output classification framework. We present an easily extensible and flexible framework which provides a real-time performance on the “in the wild” images, evaluated on a challenging “Annotated Facial Landmarks in the Wild” database. We show that our detector achieves better results than the current state of the art in terms of the localization error.

Facial Landmark Tracking by Tree-Based Deformable Part Model Based Detector

In this paper we describe a tracker of facial landmarks submitted to the 300 Videos in the Wild (300-VW) challenge. Our tracker is a straightforward extension of a well tuned tree-based DPM landmark detector originally developed for static images. The tracker is obtained by applying the static detector independently in each frame and using the Kalman filter to smooth estimates of the face positions as well as to compensate possible failures of the face detector. The resulting tracker provides a robust estimate of 68 landmarks running at 5 fps on an ordinary PC. We provide an open-source implementation of the proposed tracker at (http://cmp.felk.cvut.cz/~uricamic/clandmark/).

A 3D Approach to Facial Landmarks: Detection, Refinement, and Tracking

A real-time algorithm for accurate localization of facial landmarks in a single monocular image is proposed. The algorithm is formulated as an optimization problem, in which the sum of responses of local classifiers is maximized with respect to the camera pose by fitting a generic (not a person-specific) 3D model. The algorithm simultaneously estimates a head position and orientation and detects the facial landmarks in the image. Despite being local, we show that the basin of attraction is large to the extent it can be initialized by a scanning window face detector. Other experiments on standard datasets demonstrate that the proposed algorithm outperforms a state-of-the-art landmark detector especially for non-frontal face images, and that it is capable of reliable and stable tracking for large set of viewing angles.

Learning Detector of Malicious Network Traffic from Weak Labels

We address the problem of learning a detector of malicious behavior in network traffic. The malicious behavior is detected based on the analysis of network proxy logs that capture malware communication between client and server computers. The conceptual problem in using the standard supervised learning methods is the lack of sufficiently representative training set containing examples of malicious and legitimate communication. Annotation of individual proxy logs is an expensive process involving security experts and does not scale with constantly evolving malware. However, weak supervision can be achieved on the level of properly defined bags of proxy logs by leveraging internet domain black lists, security reports, and sandboxing analysis. We demonstrate that an accurate detector can be obtained from the collected security intelligence data by using a Multiple Instance Learning algorithm tailored to the Neyman-Pearson problem. We provide a thorough experimental evaluation on a large corpus of network communications collected from various company network environments.

Large-scale robust transductive support vector machines

In this paper, we propose a robust and fast transductive support vector machine (RTSVM) classifier that can be applied to large-scale data. To this end, we use the robust Ramp loss instead of Hinge loss for labeled data samples. The resulting optimization problem is non-convex but it can be decomposed to a convex and concave parts. Therefore, the optimization is accomplished iteratively by solving a sequence of convex problems known as concave-convex procedure. Stochastic gradient (SG) is used to solve the convex problem at each iteration, thus the proposed method scales well with large training set size for the linear case (to the best of our knowledge, it is the second transductive classification method that is practical for more than a million data). To extend the proposed method to the nonlinear case, we proposed two alternatives where one uses the primal optimization problem and the other uses the dual. But in contrast to the linear case, both alternatives do not scale well with large-scale data. Experimental results show that the proposed method achieves comparable results to other related transductive SVM methods, but it is faster than other transductive learning methods and it is more robust to the noisy data.

FASOLE: fast algorithm for structured output learning

This paper proposes a novel Fast Algorithm for Structured Ouput LEarning (FASOLE). FASOLE implements the sequential dual ascent (SDA) algorithm for solving the dual problem of the Structured Output Support Vector Machines (SO-SVM). Unlike existing instances of SDA algorithm applied for SO-SVM, the proposed FASOLE uses a different working set selection strategy which provides nearly maximal improvement of the objective function in each update. FASOLE processes examples in an on-line fashion and it provides certificate of optimality. FASOLE is guaranteed to find the e-optimal solution in

Alignment of sewerage inspection videos for their easier indexing

\mathcal{O}(\frac{1}{\varepsilon^2})

Learning data discretization via convex optimization

time in the worst case. In the empirical comparison FASOLE consistently outperforms the existing state-of-the-art solvers, like the Cutting Plane Algorithm or the Block-Coordinate Frank-Wolfe algorithm, achieving up to an order of magnitude speedups while obtaining the same precise solution.