"In computer vision and image processing, object detection algorithms are used to detect semantic objects of certain classes of images and videos. Object detector algorithms use deep learning networks to classify detected regions. Unprecedented advancements in Convolutional Neural Networks (CNN) have led to new possibilities and implementations for object detectors. An object detector which uses a deep learning algorithm detect objects through proposed regions, and then classifies the region using a CNN. Object detectors are computationally efficient unlike a typical CNN which is computationally complex and expensive. Object detectors are widely used for face detection, recognition, and object tracking. In this thesis, deep learning based object detection algorithms are implemented to classify facially expressed emotions in real-time captured through a webcam. A typical CNN would classify images without specifying regions within an image, which could be considered as a limitation towards better understanding the network performance which depend on different training options. It would also be more difficult to verify whether a network have converged and is able to generalize, which is the ability to classify unseen data, data which was not part of the training set. Fast Region-based Convolutional Neural Network, an object detection algorithm; used to detect facially expressed emotion in real-time by classifying proposed regions. The Fast R-CNN is trained using a high-quality video database, consisting of 24 actors, facially expressing eight different emotions, obtained from images which were processed from 60 videos per actor. An object detector’s performance is measured using various metrics. Regardless of how an object detector performed with respect to average precision or miss rate, doing well on such metrics would not necessarily mean that the network is correctly classifying regions. This may result from the fact that the network model has been over-trained. In our work we showed that object detector algorithm such as Fast R-CNN performed surprisingly well in classifying facially expressed emotions in real-time, performing better than CNN."--Abstract.

The world is experiencing an unprecedented period of change and growth through all the electronic and technilogical developments and everyone on the planet has been impacted. What was once ‘science fiction’, today it is a reality. This book explores the world of many of once unthinkable advancements by explaining current technologies in great detail. Each chapter focuses on a different aspect - Machine Vision, Pattern Analysis and Image Processing - Advanced Trends in Computational Intelligence and Data Analytics - Futuristic Communication Technologies - Disruptive Technologies for Future Sustainability. The chapters include the list of topics that spans all the areas of smart intelligent systems and computing such as: Data Mining with Soft Computing, Evolutionary Computing, Quantum Computing, Expert Systems, Next Generation Communication, Blockchain and Trust Management, Intelligent Biometrics, Multi-Valued Logical Systems, Cloud Computing and security etc. An extensive list of bibliographic references at the end of each chapter guides the reader to probe further into application area of interest to him/her.

This thesis presents an image-based real-time facial expression recognition system that is capable of recognizing basic facial expressions of several subjects simultaneously from a webcam. Our proposed methodology combines a supervised transfer learning strategy and a joint supervision method with a new supervision signal that is crucial for facial tasks. A convolutional neural network (CNN) model, MobileNet, that contains both accuracy and speed is deployed in both offline and real-time frameworks to enable fast and accurate real-time output. Evaluations for both offline and real-time experiments are provided in our work. The offline evaluation is carried out by first evaluating two publicly available datasets, JAFFE and CK+, and then presenting the results of the cross-dataset evaluation between these two datasets to verify the generalization ability of the proposed method. A comprehensive evaluation configuration for the CK+ dataset is given in this work, providing a baseline for a fair comparison. It reaches an accuracy of 95.24% on JAFFE dataset, and an accuracy of 96.92% on 6-class CK+ dataset which only contains the last frames of image sequences. The resulting average run-time cost for recognition in the real-time implementation is reported, which is approximately 3.57 ms/frame on an NVIDIA Quadro K4200 GPU. The results demonstrate that our proposed CNN-based framework for facial expression recognition, which does not require a massive preprocessing module, can not only achieve state-of-art accuracy on these two datasets but also perform the classification task much faster than a conventional machine learning methodology as a result of the lightweight structure of MobileNet.

Automatic facial expression recognition (FER) and emotion recognition have aroused many researchers' interest in a variety of research fields because of an important role in human centered interfaces and the advent of cheap and powerful computer and video camera in the last decade. In addition, the emergence of the smartphones era has aroused considerable interest in the mobile application development in connection with facial expression and emotion recognition. However, in spite of the enhanced hardware of recent smartphones, mobile applications for processing real-time video should always consider limited resources available in smartphones. The limited processing resources in smartphones still make it difficult to directly adopt the existing facial expression and emotion recognition system from desktops. Most studies for FER have been carried out and evaluated under restricted experimental environment. For instance, some approaches deal with only static images or work with video sequences manually pre-segmented (temporally) for each expression. However, the temporal segmentation of expressions is the most essential element in automatic FER systems as real world applications for real-time video. Also, the real world dataset for FER is different from most conventional datasets which are mainly collected in a limited experimental environment. It is hard to apply models made with datasets collected under lab environment to real world application. The automatic FER should be capable of satisfying these various types of noisy datasets. We address several problems for real-time FER on low-power smartphones. First, we presents a real-time FER effectively running on smartphones. The system employs a set of Support Vector Machines (SVM) for neutral expression and 6 basic emotions with 13D geometric facial features including temporal information. We evaluated the performance of the proposed system in terms of speed and accuracy on offline dataset and commercial off-the-shelf smartphones. Second, we present a real-time temporal video segmenting approach for automatic FER applicable in a smartphone. The proposed system uses a Finite State Machine (FSM) for segmenting real time video into temporal phases from neutral expression to the peak of an expression. The system performs FER with SVM on every apex state after automatic temporal segmentation, without any sampling time delay. Third, we present gender-driven ensemble models for FER on smartphones working with a context-sensitive multimedia content recommendation system. Based on the fact that male and female express an emotion with a distinct difference in the horizontal and vertical facial movements, we employ the ensemble model with three weak classifiers trained by gender-specific subsets and a general dataset of facial expression. In the system, users receive feedback by links to multimedia contents such as videos, photos and e-books regarding a current user's emotion. Last, we present an approach using CNN model for FER to accommodate noisy images and videos dataset in real world environment. We adopt FER2013 dataset for training CNN model. We show the CNN model is able to work very well for expression recognition even with real, noisy data that is not used for training.

The fields of intelligent systems and sustainability have been gaining momentum in the research community. They have drawn interest in such research fields as computer science, information technology, electrical engineering, and other associated engineering disciples. The promise of intelligent systems applied to sustainability is becoming a reality due to the recent advancements in the Internet of Things (IoT), Artificial Intelligence, Big Data, blockchain, deep learning, and machine learning. The emergence of intelligent systems has given rise to a wide range of techniques and algorithms using an ensemble approach to implement novel solutions for complex problems associated with sustainability. Intelligent Systems and Sustainable Computational Models: Concepts, Architecture, and Practical Applications explores this ensemble approach towards building a sustainable future. It explores novel solutions for such pressing problems as smart healthcare ecosystems, energy efficient distributed computing, affordable renewable resources, mitigating financial risks, monitoring environmental degradation, and balancing climate conditions. The book helps researchers to apply intelligent systems to computational sustainability models to propose efficient methods, techniques, and tools. The book covers such areas as: Intelligent and adaptive computing for sustainable energy, water, and transportation networks Blockchain for decentralized systems for sustainable applications, systems, and infrastructure IoT for sustainable critical infrastructure Explainable AI (XAI) and decision-making models for computational sustainability Sustainable development using edge computing, fog computing and cloud computing Cognitive intelligent systems for e-learning Artificial Intelligence and machine learning for large scale data Green computing and cyber physical systems Real-time applications in healthcare, agriculture, smart cities, and smart governance. By examining how intelligent systems can build a sustainable society, the book presents systems solutions that can benefit researchers and professionals in such fields as information technology, health, energy, agricultural, manufacturing, and environmental protection.

The two-volume set CCIS 662 and CCIS 663 constitutes the refereed proceedings of the 7th Chinese Conference on Pattern Recognition, CCPR 2016, held in Chengdu, China, in November 2016.The 121 revised papers presented in two volumes were carefully reviewed and selected from 199 submissions. The papers are organized in topical sections on robotics; computer vision; basic theory of pattern recognition; image and video processing; speech and language; emotion recognition.