فهرست مقالات Facial expression Recognition

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  1 - Facial expression recognition based on Local Binary Patterns
  Saeede Jabbarzadeh Reyhani Saeed Meshgini
  Classical LBP such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. In this paper, we introduce an improved LBP algorithm to solve these problems that utilizes Fast PCA algorithm for reduction of vector چکیده کامل

  Classical LBP such as complexity and high dimensions of feature vectors that make it necessary to apply dimension reduction processes. In this paper, we introduce an improved LBP algorithm to solve these problems that utilizes Fast PCA algorithm for reduction of vector dimensions of extracted features. In other words, proffer method (Fast PCA+LBP) is an improved LBP algorithm that is extracted from classical LBP operator. In this method, first circular neighbor operator is used for features extraction of facial expression. Then, an algorithm of Fast PCA is used for reduction of feature vector dimensions. Simulation results show that the proposed method in this paper in terms of accuracy and speed of recognition, has had a better performance compared with the same algorithm. پرونده مقاله
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  2 - Facial Expression Recognition Based on Structural Changes in Facial Skin
  Zeynab Shokoohi Karim Faez
  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 ar چکیده کامل

  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 advances, facial recognition has become more accessible and is now a key technique to be employed and used in creating more natural man-machine interactions, Computer vision, and health care. In this paper, we empirically evaluate facial representation based on statistical local features, Local Binary Patterns, for person-independent facial expression recognition. Different machine learning methods are systematically examined on several databases. Extensive experiments illustrate that LBP features are effective and efficient for facial expression recognition. In this paper, we proposed a face expression detection method based on the difference of a face expression andthe allocated special pattern to each expression. The analysis of the image detection system locally and through a sliding window (sliding) at multiple scales, are estimated. Multiple scales are extracted aslocally binary features. Through using the change point between windows, points of face are getting a directional movement. Through using points movement of whole facial expressions and rating system that is created thesuperfluouspoints are eliminated. The classifications are taken based on the nearest neighbor.To sum up this paper, the proposed algorithms are tested on Cohn-Kanade data set and the results showed the best performance and reliability into other algorithms. We investigated LBP features for the facial skin structural changes, which is seldom addressed in the existing literature. پرونده مقاله
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  3 - Facial Expression Recognition Using Texture and Edge Descriptors
  Davar Giveki Nastaran Mirzaei
  AbstractFacial expression recognition is one of the most important computer vision issues that has many applications. One of them is the Human computer interaction. In this paper, a method for facial expression recognition using texture and edge descriptors is proposed. چکیده کامل

  AbstractFacial expression recognition is one of the most important computer vision issues that has many applications. One of them is the Human computer interaction. In this paper, a method for facial expression recognition using texture and edge descriptors is proposed. Facial expression recognition generally consists of three steps: preprocessing, feature extraction and classification. In this paper, histogram Equalization has been used in the proposed method for pre-process the input images in which the face is present. In this paper, the focus is on the feature extraction and a combination of LDP1 and HOG2 descriptors has been used to improve the existing methods. After feature extraction, the support vector machine was used to classification the facial expression recognition. This article uses the JAFFE database. The database contains 213 images of seven facial expressions (happy, sad, angry, fear, disgust, surprised and natural) taken from 10 Japanese female models. The results showed that the proposed method with 99.04% accuracy in the facial recognition test had a better performance than the methods of previous researchers. پرونده مقاله