Survey on encode biometric data for transmission in wireless communication networks

The aim of this research survey is to review an enhanced model supported by artificial intelligence to encode biometric data for transmission in wireless communication networks can be tricky as performance decreases with increasing size due to interference, especially if channels and network topology are not selected carefully beforehand. Additionally, network dissociations may occur easily if crucial links fail as redundancy is neglected for signal transmission. Therefore, we present several algorithms and its implementation which addresses this problem by finding a network topology and channel assignment that minimizes interference and thus allows a deployment to increase its throughput performance by utilizing more bandwidth in the local spectrum by reducing coverage as well as connectivity issues in multiple AI-based techniques. Our evaluation survey shows an increase in throughput performance of up to multiple times or more compared to a baseline scenario where an optimization has not taken place and only one channel for the whole network is used with AI-based techniques. Furthermore, our solution also provides a robust signal transmission which tackles the issue of network partition for coverage and for single link failures by using airborne wireless network. The highest end-to-end connectivity stands at 10 Mbps data rate with a maximum propagation distance of several kilometers. The transmission in wireless network coverage depicted with several signal transmission data rate with 10 Mbps as it has lowest coverage issue with moderate range of propagation distance using enhanced model to encode biometric data for transmission in wireless communication

Productivity Measurement of Call Centre Agents using a Multimodal Classification Approach

Call centre channels play a cornerstone role in business communications and transactions, especially in challenging business situations. Operations’ efficiency, service quality, and resource productivity are core aspects of call centres’ competitive advantage in rapid market competition. Performance evaluation in call centres is challenging due to human subjective evaluation, manual assortment to massive calls, and inequality in evaluations because of different raters. These challenges impact these operations' efficiency and lead to frustrated customers. This study aims to automate performance evaluation in call centres using various deep learning approaches. Calls recorded in a call centre are modelled and classified into high- or low-performance evaluations categorised as productive or nonproductive calls. The proposed conceptual model considers a deep learning network approach to model the recorded calls as text and speech. It is based on the following: 1) focus on the technical part of agent performance, 2) objective evaluation of the corpus, 3) extension of features for both text and speech, and 4) combination of the best accuracy from text and speech data using a multimodal structure. Accordingly, the diarisation algorithm extracts that part of the call where the agent is talking from which the customer is doing so. Manual annotation is also necessary to divide the modelling corpus into productive and nonproductive (supervised training). Krippendorff’s alpha was applied to avoid subjectivity in the manual annotation. Arabic speech recognition is then developed to transcribe the speech into text. The text features are the words embedded using the embedding layer. The speech features make several attempts to use the Mel Frequency Cepstral Coefficient (MFCC) upgraded with Low-Level Descriptors (LLD) to improve classification accuracy. The data modelling architectures for speech and text are based on CNNs, BiLSTMs, and the attention layer. The multimodal approach follows the generated models to improve performance accuracy by concatenating the text and speech models using the joint representation methodology. The main contributions of this thesis are: • Developing an Arabic Speech recognition method for automatic transcription of speech into text. • Drawing several DNN architectures to improve performance evaluation using speech features based on MFCC and LLD. • Developing a Max Weight Similarity (MWS) function to outperform the SoftMax function used in the attention layer. • Proposing a multimodal approach for combining the text and speech models for best performance evaluation

Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions

Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design computer agents with intelligent capabilities such as understanding, reasoning, and learning through integrating multiple communicative modalities, including linguistic, acoustic, visual, tactile, and physiological messages. With the recent interest in video understanding, embodied autonomous agents, text-to-image generation, and multisensor fusion in application domains such as healthcare and robotics, multimodal machine learning has brought unique computational and theoretical challenges to the machine learning community given the heterogeneity of data sources and the interconnections often found between modalities. However, the breadth of progress in multimodal research has made it difficult to identify the common themes and open questions in the field. By synthesizing a broad range of application domains and theoretical frameworks from both historical and recent perspectives, this paper is designed to provide an overview of the computational and theoretical foundations of multimodal machine learning. We start by defining two key principles of modality heterogeneity and interconnections that have driven subsequent innovations, and propose a taxonomy of 6 core technical challenges: representation, alignment, reasoning, generation, transference, and quantification covering historical and recent trends. Recent technical achievements will be presented through the lens of this taxonomy, allowing researchers to understand the similarities and differences across new approaches. We end by motivating several open problems for future research as identified by our taxonomy

Automatic Emotion Recognition: Quantifying Dynamics and Structure in Human Behavior.

Emotion is a central part of human interaction, one that has a huge influence on its overall tone and outcome. Today's human-centered interactive technology can greatly benefit from automatic emotion recognition, as the extracted affective information can be used to measure, transmit, and respond to user needs. However, developing such systems is challenging due to the complexity of emotional expressions and their dynamics in terms of the inherent multimodality between audio and visual expressions, as well as the mixed factors of modulation that arise when a person speaks. To overcome these challenges, this thesis presents data-driven approaches that can quantify the underlying dynamics in audio-visual affective behavior. The first set of studies lay the foundation and central motivation of this thesis. We discover that it is crucial to model complex non-linear interactions between audio and visual emotion expressions, and that dynamic emotion patterns can be used in emotion recognition. Next, the understanding of the complex characteristics of emotion from the first set of studies leads us to examine multiple sources of modulation in audio-visual affective behavior. Specifically, we focus on how speech modulates facial displays of emotion. We develop a framework that uses speech signals which alter the temporal dynamics of individual facial regions to temporally segment and classify facial displays of emotion. Finally, we present methods to discover regions of emotionally salient events in a given audio-visual data. We demonstrate that different modalities, such as the upper face, lower face, and speech, express emotion with different timings and time scales, varying for each emotion type. We further extend this idea into another aspect of human behavior: human action events in videos. We show how transition patterns between events can be used for automatically segmenting and classifying action events. Our experimental results on audio-visual datasets show that the proposed systems not only improve performance, but also provide descriptions of how affective behaviors change over time. We conclude this dissertation with the future directions that will innovate three main research topics: machine adaptation for personalized technology, human-human interaction assistant systems, and human-centered multimedia content analysis.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133459/1/yelinkim_1.pd

Emotion Recognition with Deep Neural Networks

RÉSUMÉ La reconnaissance automatique des émotions humaines a été étudiée pendant des décennies. Il est l'un des éléments clés de l'interaction homme-ordinateur dans les domaines des soins de santé, de l'éducation, du divertissement et de la publicité. La reconnaissance des émotions est une tâche difficile car elle repose sur la prédiction des états émotionnels abstraits à partir de données d'entrée multimodales. Ces modalités comprennent la vidéo, l’audio et des signaux physiologiques. La modalité visuelle est l'un des canaux les plus informatifs. Notons en particulier les expressions du visage qui sont un très fort indicateur de l'état émotionnel d'un sujet. Un système automatisé commun de reconnaissance d'émotion comprend plusieurs étapes de traitement, dont chacune doit être réglée et intégrée dans un pipeline. Ces pipelines sont souvent ajustés à la main, et ce processus peut introduire des hypothèses fortes sur les propriétés de la tâche et des données. Limiter ces hypothèses et utiliser un apprentissage automatique du pipeline de traitement de données donne souvent des solutions plus générales. Au cours des dernières années, il a été démontré que les méthodes d'apprentissage profond mènent à de bonnes représentations pour diverses modalités. Pour de nombreux benchmarks, l'écart diminue rapidement entre les algorithmes de pointe basés sur des réseaux neuronaux profonds et la performance humaine. Ces réseaux apprennent hiérarchies de caractéristiques. Avec la profondeur croissante, ces hiérarchies peuvent décrire des concepts plus abstraits. Cette progrès suggèrent d'explorer les applications de ces méthodes d'apprentissage à l'analyse du visage et de la reconnaissance des émotions. Cette thèse repose sur une étude préliminaire et trois articles, qui contribuent au domaine de la reconnaissance des émotions. L'étude préliminaire présente une nouvelle variante de Patterns Binaires Locales (PBL), qui est utilisé comme une représentation binaire de haute dimension des images faciales. Il est commun de créer des histogrammes de caractéristiques de PBL dans les régions d'images d'entrée. Toutefois, dans ce travail, ils sont utilisés en tant que vecteurs binaires de haute dimension qui sont extraits à des échelles multiples autour les points clés faciales détectées. Nous examinons un pipeline constitué de la réduction de la dimensionnalité non supervisé et supervisé, en utilisant l'Analyse en Composantes Principales (ACP) et l'Analyse Discriminante Fisher Locale (ADFL), suivi d'une Machine à Vecteurs de Support (MVS) comme classificateur pour la prédiction des expressions faciales. Les expériences montrent que les étapes de réduction de dimensionnalité fournissent de la robustesse en présence de bruit dans points clés. Cette approche atteint, lors de sa publication, des performances de l’état de l’art dans la reconnaissance de l'expression du visage sur l’ensemble de données Extended Cohn-Kanade (CK+) (Lucey et al, 2010) et sur la détection de sourire sur l’ensemble de données GENKI (GENKI-4K, 2008). Pour la tâche de détection de sourire, un profond Réseau Neuronal Convolutif (RNC) a été utilisé pour référence fiable. La reconnaissance de l'émotion dans les vidéos semblable à ceux de la vie de tous les jours, tels que les clips de films d'Hollywood dans l'Emotion Recognition in the Wild (EmotiW) challenge (Dhall et al, 2013), est beaucoup plus difficile que dans des environnements de laboratoire contrôlées. Le premier article est une analyse en profondeur de la entrée gagnante de l'EmotiW 2013 challenge (Kahou et al, 2013) avec des expériments supplémentaires sur l'ensemble de données du défi de l’an 2014. Le pipeline est constitué d'une combinaison de modèles d'apprentissage en profondeur, chacun spécialisé dans une modalité. Ces modèles comprennent une nouvelle technique d’agrégation de caractéristiques d’images individuelles pour permettre de transférer les caractéristiques apprises par réseaux convolutionnels (CNN) sur un grand ensemble de données d’expressions faciales, et de les application au domaine de l’analyse de contenu vidéo. On y trouve aussi un ``deep belief net'' (DBN) pour les caractéristiques audio, un pipeline de reconnaissance d’activité pour capturer les caractéristiques spatio-temporelles, ainsi qu’modèle de type ``bag-of-mouths'' basé sur k-means pour extraire les caractéristiques propres à la bouche. Plusieurs approches pour la fusion des prédictions des modèles spécifiques à la modalité sont comparés. La performance après un nouvel entraînement basé sur les données de 2014, établis avec quelques adaptations, est toujours comparable à l’état de l’art actuel. Un inconvénient de la méthode décrite dans le premier article est l'approche de l'agrégation de la modalité visuelle qui implique la mise en commun par image requiert un vecteur de longueur fixe. Cela ne tient pas compte de l'ordre temporel à l'intérieur des segments groupés. Les Réseau de Neurones Récurrents (RNR) sont des réseaux neuronaux construits pour le traitement séquentiel des données. Ils peuvent résoudre ce problème en résumant les images dans un vecteur de valeurs réelles qui est mis à jour à chaque pas de temps. En général, les RNR fournissent une façon d'apprendre une approche d'agrégation d'une manière axée sur les données. Le deuxième article analyse l'application d'un RNR sur les caractéristiques issues d’un réseau neuronal de convolution utilisé pour la reconnaissance des émotions dans la vidéo. Une comparaison de la RNR avec l'approche fondée sur pooling montre une amélioration significative des performances de classification. Il comprend également une fusion au niveau de la caractéristiques et au niveau de décision de modèles pour différentes modalités. En plus d’utiliser RNR comme dans les travaux antérieurs, il utilise aussi un modèle audio basé sur MVS, ainsi que l'ancien modèle d'agrégation qui sont fusionnées pour améliorer les performances sur l'ensemble de données de défi EmotiW 2015. Cette approche a terminé en troisième position dans le concours, avec une différence de seulement 1% dans la précision de classification par rapport au modèle gagnant. Le dernier article se concentre sur un problème de vision par ordinateur plus général, à savoir le suivi visuel. Un RNR est augmenté avec un mécanisme d'attention neuronal qui lui permet de se concentrer sur l'information liée à une tâche, ignorant les distractions potentielles dans la trame vidéo d'entrée. L'approche est formulée dans un cadre neuronal modulaire constitué de trois composantes: un module d'attention récurrente qui détermine où chercher, un module d'extraction de caractéristiques fournissant une représentation de quel objet est vu, et un module objectif qui indique pourquoi un comportement attentionnel est appris. Chaque module est entièrement différentiables, ce qui permet une optimisation simple à base de gradient. Un tel cadre pourrait être utilisé pour concevoir une solution de bout en bout pour la reconnaissance de l'émotion dans la vision, ne nécessitant pas les étapes initiales de détection de visage ou de localisation d’endroits d’intérêt. L'approche est présentée dans trois ensembles de données de suivi, y compris un ensemble de données du monde réel. En résumé, cette thèse explore et développe une multitude de techniques d'apprentissage en profondeur, complétant des étapes importantes en vue de l’objectif à long terme de la construction d'un système entraînable de bout en bout pour la reconnaissance des émotions.----------ABSTRACT Automatic recognition of human emotion has been studied for decades. It is one of the key components in human computer interaction with applications in health care, education, entertainment and advertisement. Emotion recognition is a challenging task as it involves predicting abstract emotional states from multi-modal input data. These modalities include video, audio and physiological signals. The visual modality is one of the most informative channels; especially facial expressions, which have been shown to be strong cues for the emotional state of a subject. A common automated emotion recognition system includes several processing steps, each of which has to be tuned and integrated into a pipeline. Such pipelines are often hand-engineered which can introduce strong assumptions about the properties of the task and data. Limiting assumptions and learning the processing pipeline from data often yields more general solutions. In recent years, deep learning methods have been shown to be able to learn good representations for various modalities. For many computer vision benchmarks, the gap between state-of-the-art algorithms based on deep neural networks and human performance is shrinking rapidly. These networks learn hierarchies of features. With increasing depth, these hierarchies can describe increasingly abstract concepts. This development suggests exploring the applications of such learning methods to facial analysis and emotion recognition. This thesis is based on a preliminary study and three articles, which contribute to the field of emotion recognition. The preliminary study introduces a new variant of Local Binary Patterns (LBPs), which is used as a high dimensional binary representation of facial images. It is common to create histograms of LBP features within regions of input images. However, in this work, they are used as high dimensional binary vectors that are extracted at multiple scales around detected facial keypoints. We examine a pipeline consisting of unsupervised and supervised dimensionality reduction, using Principal Component Analysis (PCA) and Local Fisher Discriminant Analysis (LFDA), followed by a Support Vector Machine (SVM) classifier for prediction of facial expressions. The experiments show that the dimensionality reduction steps provide robustness in the presence of noisy keypoints. This approach achieved state-of-the-art performance in facial expression recognition on the Extended Cohn-Kanade (CK+) data set (Lucey et al, 2010) and smile detection on the GENKI data set (GENKI-4K, 2008) at the time. For the smile detection task, a deep Convolutional Neural Network (CNN) was used as a strong baseline. Emotion recognition in close-to-real-world videos, such as the Hollywood film clips in the Emotion Recognition in the Wild (EmotiW) challenge (Dhall et al, 2013), is much harder than in controlled lab environments. The first article is an in-depth analysis of the EmotiW 2013 challenge winning entry (Kahou et al, 2013) with additional experiments on the data set of the 2014 challenge. The pipeline consists of a combination of deep learning models, each specializing on one modality. The models include the following: a novel aggregation of per-frame features helps to transfer powerful CNN features learned on a large pooled data set of facial expression images to the video domain, a Deep Belief Network (DBN) learns audio features, an activity recognition pipeline captures spatio-temporal motion features and a k-means based bag-of-mouths model extracts features around the mouth region. Several approaches for fusing the predictions of modality-specific models are compared. The performance after re-training on the 2014 data set with a few adaptions is still competitive to the new state-of-the-art. One drawback of the method described in the first article is the aggregation approach of the visual modality which involves pooling per-frame features into a fixed-length vector. This ignores the temporal order inside the pooled segments. Recurrent Neural Networks (RNNs) are neural networks built for sequential processing of data, which can address this issue by summarizing frames in a real-valued state vector that is updated at each time-step. In general, RNNs provide a way of learning an aggregation approach in a data-driven manner. The second article analyzes the application of an RNN on CNN features for emotion recognition in video. A comparison of the RNN with the pooling-based approach shows a significant improvement in classification performance. It also includes a feature-level fusion and decision-level fusion of models for different modalities. In addition to the RNN, the same activity pipeline as previous work, an SVM-based audio model and the old aggregation model are fused to boost performance on the EmotiW 2015 challenge data set. This approach was the second runner-up in the challenge with a small margin of 1% in classification accuracy to the challenge winner. The last article focuses on a more general computer vision problem, namely visual tracking. An RNN is augmented with a neural attention mechanism that allows it to focus on task-related information, ignoring potential distractors in input frames. The approach is formulated in a modular neural framework consisting of three components: a recurrent attention module controlling where to look, a feature-extraction module providing a representation of what is seen and an objective module which indicates why an attentional behaviour is learned. Each module is fully differentiable allowing simple gradient-based optimization. Such a framework could be used to design an end-to-end solution for emotion recognition in vision, potentially not requiring initial steps of face detection or keypoint localization. The approach is tested on three tracking data sets including one real-world data set. In summary, this thesis explores and develops a multitude of deep learning techniques, making significant steps towards a long-term goal of building an end-to-end trainable systems for emotion recognition