Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either `spontaneous' or `posed' categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.Comment: 16 pages, 8 figures, ACCV 2016, Second Workshop on Spontaneous Facial Behavior Analysi

Improving Malware Detection Accuracy by Extracting Icon Information

Detecting PE malware files is now commonly approached using statistical and machine learning models. While these models commonly use features extracted from the structure of PE files, we propose that icons from these files can also help better predict malware. We propose an innovative machine learning approach to extract information from icons. Our proposed approach consists of two steps: 1) extracting icon features using summary statics, histogram of gradients (HOG), and a convolutional autoencoder, 2) clustering icons based on the extracted icon features. Using publicly available data and by using machine learning experiments, we show our proposed icon clusters significantly boost the efficacy of malware prediction models. In particular, our experiments show an average accuracy increase of 10% when icon clusters are used in the prediction model.Comment: Full version. IEEE MIPR 201

Vehicle Detection Based on Convolutional Neural Networks

Sõidukite täpne tuvastus ja klassifitseerimine mängib suurt rolli intelligentsete transpordisüsteemide valdkonnas. Sõidukite tuvastus liikluses võimaldab analüüsida autojuhtide käitumist ning lisaks tuvastada liikluseeskirja rikkumisi ja liiklusõnnetusi. Sõidukite tuvastus ja klassifitseerimine on keeruline ülesanne erinevate valgustingimuste, ilmastikunähtuste ja sõidukite mitmekesisuse tõttu. Mitmed olemasolevad lahendused kasutavad tunnuste eraldamise algoritme ning tugivektorklassifitseerijat. Hiljuti on konvolutsioonilised närvivõrgud osutunud paremaks lahenduseks. Antud lõputöö esitleb konvolutsioonilist tehisnärvivõrku, mis suudab liigitada ja tuvastada sõidukeid erinevate nurkade alt. Peale selle eeltöödeldakse andmeid kiire Fourier' teisenduse abil. Välja pakutud eeltöötluse mõju uuritakse selle lõputöö käigus valminud sõidukite tuvastus- ja klassifitseerimisprogrammi abil.Accurate vehicle detection or classification plays an important role in Intelligent Transportations Systems. Ability to detect vehicles in traffic scenes allows analyzing drivers' behavior as well as detect traffic offenses and accidents. Detection and classification of vehicles is a challenging task due to weather and light conditions and vehicle type diversity. Several solutions use feature extraction algorithms along with support vector machine classifier. However, convolutional neural networks have proved to be potentially more effective. In this thesis, we present a convolutional neural network trained to classify and detect vehicles from multiple angles. Moreover, Fast Fourier Transform is used during data preprocessing. The effect of such preprocessing is examined on the developed vehicle classifier and detector