Less is More: Micro-expression Recognition from Video using Apex Frame

Despite recent interest and advances in facial micro-expression research, there is still plenty room for improvement in terms of micro-expression recognition. Conventional feature extraction approaches for micro-expression video consider either the whole video sequence or a part of it, for representation. However, with the high-speed video capture of micro-expressions (100-200 fps), are all frames necessary to provide a sufficiently meaningful representation? Is the luxury of data a bane to accurate recognition? A novel proposition is presented in this paper, whereby we utilize only two images per video: the apex frame and the onset frame. The apex frame of a video contains the highest intensity of expression changes among all frames, while the onset is the perfect choice of a reference frame with neutral expression. A new feature extractor, Bi-Weighted Oriented Optical Flow (Bi-WOOF) is proposed to encode essential expressiveness of the apex frame. We evaluated the proposed method on five micro-expression databases: CAS(ME)$^2$, CASME II, SMIC-HS, SMIC-NIR and SMIC-VIS. Our experiments lend credence to our hypothesis, with our proposed technique achieving a state-of-the-art F1-score recognition performance of 61% and 62% in the high frame rate CASME II and SMIC-HS databases respectively.Comment: 14 pages double-column, author affiliations updated, acknowledgment of grant support adde

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

Reconhecimento de padrões em expressões faciais : algoritmos e aplicações

Orientador: Hélio PedriniTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O reconhecimento de emoções tem-se tornado um tópico relevante de pesquisa pela comunidade científica, uma vez que desempenha um papel essencial na melhoria contínua dos sistemas de interação humano-computador. Ele pode ser aplicado em diversas áreas, tais como medicina, entretenimento, vigilância, biometria, educação, redes sociais e computação afetiva. Há alguns desafios em aberto relacionados ao desenvolvimento de sistemas emocionais baseados em expressões faciais, como dados que refletem emoções mais espontâneas e cenários reais. Nesta tese de doutorado, apresentamos diferentes metodologias para o desenvolvimento de sistemas de reconhecimento de emoções baseado em expressões faciais, bem como sua aplicabilidade na resolução de outros problemas semelhantes. A primeira metodologia é apresentada para o reconhecimento de emoções em expressões faciais ocluídas baseada no Histograma da Transformada Census (CENTRIST). Expressões faciais ocluídas são reconstruídas usando a Análise Robusta de Componentes Principais (RPCA). A extração de características das expressões faciais é realizada pelo CENTRIST, bem como pelos Padrões Binários Locais (LBP), pela Codificação Local do Gradiente (LGC) e por uma extensão do LGC. O espaço de características gerado é reduzido aplicando-se a Análise de Componentes Principais (PCA) e a Análise Discriminante Linear (LDA). Os algoritmos K-Vizinhos mais Próximos (KNN) e Máquinas de Vetores de Suporte (SVM) são usados para classificação. O método alcançou taxas de acerto competitivas para expressões faciais ocluídas e não ocluídas. A segunda é proposta para o reconhecimento dinâmico de expressões faciais baseado em Ritmos Visuais (VR) e Imagens da História do Movimento (MHI), de modo que uma fusão de ambos descritores codifique informações de aparência, forma e movimento dos vídeos. Para extração das características, o Descritor Local de Weber (WLD), o CENTRIST, o Histograma de Gradientes Orientados (HOG) e a Matriz de Coocorrência em Nível de Cinza (GLCM) são empregados. A abordagem apresenta uma nova proposta para o reconhecimento dinâmico de expressões faciais e uma análise da relevância das partes faciais. A terceira é um método eficaz apresentado para o reconhecimento de emoções audiovisuais com base na fala e nas expressões faciais. A metodologia envolve uma rede neural híbrida para extrair características visuais e de áudio dos vídeos. Para extração de áudio, uma Rede Neural Convolucional (CNN) baseada no log-espectrograma de Mel é usada, enquanto uma CNN construída sobre a Transformada de Census é empregada para a extração das características visuais. Os atributos audiovisuais são reduzidos por PCA e LDA, então classificados por KNN, SVM, Regressão Logística (LR) e Gaussian Naïve Bayes (GNB). A abordagem obteve taxas de reconhecimento competitivas, especialmente em dados espontâneos. A penúltima investiga o problema de detectar a síndrome de Down a partir de fotografias. Um descritor geométrico é proposto para extrair características faciais. Experimentos realizados em uma base de dados pública mostram a eficácia da metodologia desenvolvida. A última metodologia trata do reconhecimento de síndromes genéticas em fotografias. O método visa extrair atributos faciais usando características de uma rede neural profunda e medidas antropométricas. Experimentos são realizados em uma base de dados pública, alcançando taxas de reconhecimento competitivasAbstract: Emotion recognition has become a relevant research topic by the scientific community, since it plays an essential role in the continuous improvement of human-computer interaction systems. It can be applied in various areas, for instance, medicine, entertainment, surveillance, biometrics, education, social networks, and affective computing. There are some open challenges related to the development of emotion systems based on facial expressions, such as data that reflect more spontaneous emotions and real scenarios. In this doctoral dissertation, we propose different methodologies to the development of emotion recognition systems based on facial expressions, as well as their applicability in the development of other similar problems. The first is an emotion recognition methodology for occluded facial expressions based on the Census Transform Histogram (CENTRIST). Occluded facial expressions are reconstructed using an algorithm based on Robust Principal Component Analysis (RPCA). Extraction of facial expression features is then performed by CENTRIST, as well as Local Binary Patterns (LBP), Local Gradient Coding (LGC), and an LGC extension. The generated feature space is reduced by applying Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms are used for classification. This method reached competitive accuracy rates for occluded and non-occluded facial expressions. The second proposes a dynamic facial expression recognition based on Visual Rhythms (VR) and Motion History Images (MHI), such that a fusion of both encodes appearance, shape, and motion information of the video sequences. For feature extraction, Weber Local Descriptor (WLD), CENTRIST, Histogram of Oriented Gradients (HOG), and Gray-Level Co-occurrence Matrix (GLCM) are employed. This approach shows a new direction for performing dynamic facial expression recognition, and an analysis of the relevance of facial parts. The third is an effective method for audio-visual emotion recognition based on speech and facial expressions. The methodology involves a hybrid neural network to extract audio and visual features from videos. For audio extraction, a Convolutional Neural Network (CNN) based on log Mel-spectrogram is used, whereas a CNN built on Census Transform is employed for visual extraction. The audio and visual features are reduced by PCA and LDA, and classified through KNN, SVM, Logistic Regression (LR), and Gaussian Naïve Bayes (GNB). This approach achieves competitive recognition rates, especially in a spontaneous data set. The second last investigates the problem of detecting Down syndrome from photographs. A geometric descriptor is proposed to extract facial features. Experiments performed on a public data set show the effectiveness of the developed methodology. The last methodology is about recognizing genetic disorders in photos. This method focuses on extracting facial features using deep features and anthropometric measurements. Experiments are conducted on a public data set, achieving competitive recognition ratesDoutoradoCiência da ComputaçãoDoutora em Ciência da Computação140532/2019-6CNPQCAPE

A spatial-temporal framework based on histogram of gradients and optical flow for facial expression recognition in video sequences

Facial expression causes different parts of the facial region to change over time and thus dynamic descriptors are inherently more suitable than static descriptors for recognising facial expressions. In this paper, we extend the spatial pyramid histogram of gradients to spatio-temporal domain to give 3-dimensional facial features and integrate them with dense optical flow to give a spatio-temporal descriptor which extracts both the spatial and dynamic motion information of facial expressions. A multi-class support vector machine based classifier with one-to-one strategy is used to recognise facial expressions. Experiments on the CK+ and MMI datasets using leave-one-out cross validation scheme demonstrate that the integrated framework achieves a better performance than using individual descriptor separately. Compared with six state of the art methods, the proposed framework demonstrates a superior performance

Fusing dynamic deep learned features and handcrafted features for facial expression recognition

The automated recognition of facial expressions has been actively researched due to its wide-ranging applications. The recent advances in deep learning have improved the performance facial expression recognition (FER) methods. In this paper, we propose a framework that combines discriminative features learned using convolutional neural networks and handcrafted features that include shape- and appearance-based features to further improve the robustness and accuracy of FER. In addition, texture information is extracted from facial patches to enhance the discriminative power of the extracted textures. By encoding shape, appearance, and deep dynamic information, the proposed framework provides high performance and outperforms state-of-the-art FER methods on the CK+ dataset