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.

This book constitutes the proceedings of the Third Workshop on Video Analytics for Audience Measurement, VAAM 2016, and the Second International Workshop on Face and Facial Expression Recognition from Real World Videos, FFER 2016, held at the 23rd International Conference on Pattern Recognition, ICPR 2016, in Cancun, Mexico, in December 2016. The 11 papers presented in this volume were carefully reviewed and selected from 13 submissions. They deal with: re-identification; consumer behavior analysis; utilizing pupillary response for task difficulty measurement; logo detection; saliency prediction; classification of facial expressions; face recognition; face verification; age estimation; super resolution; pose estimation; and pain recognition.

Face perception is the process by which the brain and mind understand and interpret the face, particularly the human face. The face is an important site for the identification of others and conveys significant social information. Probably because of the importance of its role in social interaction, psychological processes involved in face perception are known to be present from birth, to be complex, and to involve large and widely distributed areas in the brain. These parts of the brain can be damaged to cause a specific impairment in understanding faces known as prosopagnosia. This book presents the latest research in the field.

This book constitutes the thoroughly refereed conference proceedings of the International Workshop on Face and Facial Expression Recognition from Real World Videos in conjunction with the 22nd International Conference on Pattern Recognition held in Stockholm, Sweden, in August 2014. The 11 revised full papers were carefully reviewed and selected from numerous submissions and cover topics such as Face Recognition, Face Alignment, Facial Expression Recognition and Facial Images.

Computer vision algorithms for the analysis of video data are obtained from a camera aimed at the user of an interactive system. It is potentially useful to enhance the interface between users and machines. These image sequences provide information from which machines can identify and keep track of their users, recognize their facial expressions and gestures, and complement other forms of human-computer interfaces. Facial Analysis from Continuous Video with Applications to Human-Computer Interfaces presents a learning technique based on information-theoretic discrimination which is used to construct face and facial feature detectors. This book also describes a real-time system for face and facial feature detection and tracking in continuous video. Finally, this book presents a probabilistic framework for embedded face and facial expression recognition from image sequences. Facial Analysis from Continuous Video with Applications to Human-Computer Interfaces is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.

This book presents the state-of-the-art in face detection and analysis. It outlines new research directions, including in particular psychology-based facial dynamics recognition, aimed at various applications such as behavior analysis, deception detection, and diagnosis of various psychological disorders. Topics of interest include face and facial landmark detection, face recognition, facial expression and emotion analysis, facial dynamics analysis, face classification, identification, and clustering, and gaze direction and head pose estimation, as well as applications of face analysis.

This book constitutes the proceedings of the Third Workshop on Face and Facial Expression Recognition from Real World Videos, FFER 2018, and the Second International Workshop on Deep Learning for Pattern Recognition, DLPR 2018, held at the 24th International Conference on Pattern Recognition, ICPR 2018, in Beijing, China, in August 2018. The 7 papers presented in this volume were carefully reviewed and selected from 9 submissions. They deal with topics such as histopathological images, action recognition, scene text detection, speech recognition, object classification, presentation attack detection, and driver drowsiness detection.