Dynamic Model of Facial Expression Recognition based on Eigen-face Approach
Emotions are best way of communicating information; and sometimes it carry more information than words. Recently, there has been a huge interest in automatic recognition of human emotion because of its wide spread application in security, surveillance, marketing, advertisement, and human-computer interaction. To communicate with a computer in a natural way, it will be desirable to use more natural modes of human communication based on voice, gestures and facial expressions. In this paper, a holistic approach for facial expression recognition is proposed which captures the variation in facial features in temporal domain and classifies the sequence of images in different emotions. The proposed method uses Haar-like features to detect face in an image. The dimensionality of the eigenspace is reduced using Principal Component Analysis (PCA). By projecting the subsequent face images into principal eigen directions, the variation pattern of the obtained weight vector is modeled to classify it into different emotions. Owing to the variations of expressions for different people and its intensity, a person specific method for emotion recognition is followed. Using the gray scale images of the frontal face, the system is able to classify four basic emotions such as happiness, sadness, surprise, and anger.
Code (0)
등록된 구현이 없습니다.
Tasks
Emotion RecognitionFacial Expression RecognitionFacial Expression Recognition (FER)MarketingSimilar Papers 제목 키워드 기반
A Statistical Nonparametric Approach of Face Recognition: Combination of Eigenface & Modified k-Means Clustering
Facial expressions convey non-verbal cues, which play an important role in interpersonal relations. Automatic recognition of human face based on facial expression can be an important component of natural human-machine in…
ClusteringFace RecognitionFacial Expressions recognition Based on Principal Component Analysis (PCA)
The facial expression recognition is an ocular task that can be performed without human discomfort, is really a speedily growing on the computer research field. There are many applications and programs uses facial expres…
Facial Expression RecognitionFacial Expression Recognition (FER)Face identification by means of a neural net classifier
This paper describes a novel face identification method that combines the eigenfaces theory with the Neural Nets. We use the eigenfaces methodology in order to reduce the dimensionality of the input image, and a neural n…
Face IdentificationFace Recognition Machine Vision System Using Eigenfaces
Face Recognition is a common problem in Machine Learning. This technology has already been widely used in our lives. For example, Facebook can automatically tag people's faces in images, and also some mobile devices use …
Face RecognitionTAGFacial Expression Detection using Patch-based Eigen-face Isomap Networks
Automated facial expression detection problem pose two primary challenges that include variations in expression and facial occlusions (glasses, beard, mustache or face covers). In this paper we introduce a novel automate…
ClassificationClusteringGeneral Classification