Facial Emotion Recognition Using Machine Learning

Face detection has been around for ages. Taking a step forward, human emotion displayed by face and felt by brain, captured in either video, electric signal (EEG) or image form can be approximated. Human emotion detection is the need of the hour so that modern artificial intelligent systems can emulate and gauge reactions from face. This can be helpful to make informed decisions be it regarding identification of intent, promotion of offers or security related threats. Recognizing emotions from images or video is a trivial task for human eye, but proves to be very challenging for machines and requires many image processing techniques for feature extraction. Several machine learning algorithms are suitable for this job. Any detection or recognition by machine learning requires training algorithm and then testing them on a suitable dataset. This paper explores a couple of machine learning algorithms as well as feature extraction techniques which would help us in accurate identification of the human emotion

Face Emotion Recognition Based on Machine Learning: A Review

Computers can now detect, understand, and evaluate emotions thanks to recent developments in machine learning and information fusion. Researchers across various sectors are increasingly intrigued by emotion identification, utilizing facial expressions, words, body language, and posture as means of discerning an individual's emotions. Nevertheless, the effectiveness of the first three methods may be limited, as individuals can consciously or unconsciously suppress their true feelings. This article explores various feature extraction techniques, encompassing the development of machine learning classifiers like k-nearest neighbour, naive Bayesian, support vector machine, and random forest, in accordance with the established standard for emotion recognition. The paper has three primary objectives: firstly, to offer a comprehensive overview of effective computing by outlining essential theoretical concepts; secondly, to describe in detail the state-of-the-art in emotion recognition at the moment; and thirdly, to highlight important findings and conclusions from the literature, with an emphasis on important obstacles and possible future paths, especially in the creation of state-of-the-art machine learning algorithms for the identification of emotions

Recognition of human activities and expressions in video sequences using shape context descriptor

The recognition of objects and classes of objects is of importance in the field of computer vision due to its applicability in areas such as video surveillance, medical imaging and retrieval of images and videos from large databases on the Internet. Effective recognition of object classes is still a challenge in vision; hence, there is much interest to improve the rate of recognition in order to keep up with the rising demands of the fields where these techniques are being applied. This thesis investigates the recognition of activities and expressions in video sequences using a new descriptor called the spatiotemporal shape context. The shape context is a well-known algorithm that describes the shape of an object based upon the mutual distribution of points in the contour of the object; however, it falls short when the distinctive property of an object is not just its shape but also its movement across frames in a video sequence. Since actions and expressions tend to have a motion component that enhances the capability of distinguishing them, the shape based information from the shape context proves insufficient. This thesis proposes new 3D and 4D spatiotemporal shape context descriptors that incorporate into the original shape context changes in motion across frames. Results of classification of actions and expressions demonstrate that the spatiotemporal shape context is better than the original shape context at enhancing recognition of classes in the activity and expression domains