2,390 research outputs found
EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to
assign one of seven emotions to short video clips extracted from Hollywood
style movies. The videos depict acted-out emotions under realistic conditions
with a large degree of variation in attributes such as pose and illumination,
making it worthwhile to explore approaches which consider combinations of
features from multiple modalities for label assignment. In this paper we
present our approach to learning several specialist models using deep learning
techniques, each focusing on one modality. Among these are a convolutional
neural network, focusing on capturing visual information in detected faces, a
deep belief net focusing on the representation of the audio stream, a K-Means
based "bag-of-mouths" model, which extracts visual features around the mouth
region and a relational autoencoder, which addresses spatio-temporal aspects of
videos. We explore multiple methods for the combination of cues from these
modalities into one common classifier. This achieves a considerably greater
accuracy than predictions from our strongest single-modality classifier. Our
method was the winning submission in the 2013 EmotiW challenge and achieved a
test set accuracy of 47.67% on the 2014 dataset
Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories
In this paper, we propose a new approach for facial expression recognition
using deep covariance descriptors. The solution is based on the idea of
encoding local and global Deep Convolutional Neural Network (DCNN) features
extracted from still images, in compact local and global covariance
descriptors. The space geometry of the covariance matrices is that of Symmetric
Positive Definite (SPD) matrices. By conducting the classification of static
facial expressions using Support Vector Machine (SVM) with a valid Gaussian
kernel on the SPD manifold, we show that deep covariance descriptors are more
effective than the standard classification with fully connected layers and
softmax. Besides, we propose a completely new and original solution to model
the temporal dynamic of facial expressions as deep trajectories on the SPD
manifold. As an extension of the classification pipeline of covariance
descriptors, we apply SVM with valid positive definite kernels derived from
global alignment for deep covariance trajectories classification. By performing
extensive experiments on the Oulu-CASIA, CK+, and SFEW datasets, we show that
both the proposed static and dynamic approaches achieve state-of-the-art
performance for facial expression recognition outperforming many recent
approaches.Comment: A preliminary version of this work appeared in "Otberdout N, Kacem A,
Daoudi M, Ballihi L, Berretti S. Deep Covariance Descriptors for Facial
Expression Recognition, in British Machine Vision Conference 2018, BMVC 2018,
Northumbria University, Newcastle, UK, September 3-6, 2018. ; 2018 :159."
arXiv admin note: substantial text overlap with arXiv:1805.0386
Emotion Recognition from Acted and Spontaneous Speech
Dizertační práce se zabývá rozpoznáním emočního stavu mluvčích z řečového signálu. Práce je rozdělena do dvou hlavních častí, první část popisuju navržené metody pro rozpoznání emočního stavu z hraných databází. V rámci této části jsou představeny výsledky rozpoznání použitím dvou různých databází s různými jazyky. Hlavními přínosy této části je detailní analýza rozsáhlé škály různých příznaků získaných z řečového signálu, návrh nových klasifikačních architektur jako je například „emoční párování“ a návrh nové metody pro mapování diskrétních emočních stavů do dvou dimenzionálního prostoru. Druhá část se zabývá rozpoznáním emočních stavů z databáze spontánní řeči, která byla získána ze záznamů hovorů z reálných call center. Poznatky z analýzy a návrhu metod rozpoznání z hrané řeči byly využity pro návrh nového systému pro rozpoznání sedmi spontánních emočních stavů. Jádrem navrženého přístupu je komplexní klasifikační architektura založena na fúzi různých systémů. Práce se dále zabývá vlivem emočního stavu mluvčího na úspěšnosti rozpoznání pohlaví a návrhem systému pro automatickou detekci úspěšných hovorů v call centrech na základě analýzy parametrů dialogu mezi účastníky telefonních hovorů.Doctoral thesis deals with emotion recognition from speech signals. The thesis is divided into two main parts; the first part describes proposed approaches for emotion recognition using two different multilingual databases of acted emotional speech. The main contributions of this part are detailed analysis of a big set of acoustic features, new classification schemes for vocal emotion recognition such as “emotion coupling” and new method for mapping discrete emotions into two-dimensional space. The second part of this thesis is devoted to emotion recognition using multilingual databases of spontaneous emotional speech, which is based on telephone records obtained from real call centers. The knowledge gained from experiments with emotion recognition from acted speech was exploited to design a new approach for classifying seven emotional states. The core of the proposed approach is a complex classification architecture based on the fusion of different systems. The thesis also examines the influence of speaker’s emotional state on gender recognition performance and proposes system for automatic identification of successful phone calls in call center by means of dialogue features.
Facial emotion recognition using min-max similarity classifier
Recognition of human emotions from the imaging templates is useful in a wide
variety of human-computer interaction and intelligent systems applications.
However, the automatic recognition of facial expressions using image template
matching techniques suffer from the natural variability with facial features
and recording conditions. In spite of the progress achieved in facial emotion
recognition in recent years, the effective and computationally simple feature
selection and classification technique for emotion recognition is still an open
problem. In this paper, we propose an efficient and straightforward facial
emotion recognition algorithm to reduce the problem of inter-class pixel
mismatch during classification. The proposed method includes the application of
pixel normalization to remove intensity offsets followed-up with a Min-Max
metric in a nearest neighbor classifier that is capable of suppressing feature
outliers. The results indicate an improvement of recognition performance from
92.85% to 98.57% for the proposed Min-Max classification method when tested on
JAFFE database. The proposed emotion recognition technique outperforms the
existing template matching methods
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