Recognition of Facial Expressions Based on Tracking and Selection of Discriminative Geometric Features
نویسندگان
چکیده
In this paper, we present a method for fully automatic facial expression recognition in facial image sequences using feature extracted from tracking of facial landmarks. The facial landmarks at the first frame of the image sequence under examination are initialized using elastic bunch graph matching (EBGM) algorithm and tracked in the consecutive video frame over time. At first, the most discriminative geometric features in terms of triangle are selected using multi-class AdaBoost with extreme learning machine (ELM) classifier. The features for facial expression recognition (FER) are extracted from AdaBoost selected most discriminative set of triangles composed of facial landmarks. Finally, the facial expressions are recognized using support vector machines (SVM) classification. The results on the extended Cohn-Kanade (CK+) and Multimedia Understanding Group (MUG) facial expression database shows a recognition accuracy of 97.80% and 95.50% respectively using proposed facial expression recognition system.
منابع مشابه
Analysis and Synthesis of Facial Expressions by Feature-Points Tracking and Deformable Model
Face expression recognition is useful for designing new interactive devices offering the possibility of new ways for human to interact with computer systems. In this paper we develop a facial expressions analysis and synthesis system. The analysis part of the system is based on the facial features extracted from facial feature points (FFP) in frontal image sequences. Selected facial feature poi...
متن کاملFacial Expression Recognition Based on Structural Changes in Facial Skin
Facial expressions are the most powerful and direct means of presenting human emotions and feelings and offer a window into a persons’ state of mind. In recent years, the study of facial expression and recognition has gained prominence; as industry and services are keen on expanding on the potential advantages of facial recognition technology. As machine vision and artificial intelligence advan...
متن کاملFacial Expression Recognition Based on Anatomical Structure of Human Face
Automatic analysis of human facial expressions is one of the challenging problems in machine vision systems. It has many applications in human-computer interactions such as, social signal processing, social robots, deceit detection, interactive video and behavior monitoring. In this paper, we develop a new method for automatic facial expression recognition based on facial muscle anatomy and hum...
متن کاملIntroducing a method for extracting features from facial images based on applying transformations to features obtained from convolutional neural networks
In pattern recognition, features are denoting some measurable characteristics of an observed phenomenon and feature extraction is the procedure of measuring these characteristics. A set of features can be expressed by a feature vector which is used as the input data of a system. An efficient feature extraction method can improve the performance of a machine learning system such as face recognit...
متن کاملMental Arithmetic Task Recognition Using Effective Connectivity and Hierarchical Feature Selection From EEG Signals
Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...
متن کامل