Emotion recognition using facial feature extraction

Computerized emotion recognition systems can be powerful tools to help solve problems in a wide range of fields including education, healthcare, and marketing. Existing systems use digital images or live video to track facial expressions on a person\u27s face and deduce that person\u27s emotional state. The research presented in this thesis explores combinations of several facial feature extraction techniques with different classifier algorithms. Namely, the feature extraction techniques used in this research were Discrete Cosine/Sine Transforms, Fast Walsh-Hadamard Transform, Principle Component Analysis, and a novel method called XPoint. Features were extracted from both global (using the entire facial image) and local (using only facial regions like the mouth or eyes) contexts and classified with Linear Discriminant Analysis and k-Nearest Neighbor algorithms. Some experiments also fused many of these features into one system in an effort to create even more accurate systems. The system accuracy for each feature extraction method/classifier combination was calculated and discussed. The combinations that performed the best produced systems between 85%-90% accurate. The most accurate systems utilized Discrete Sine Transform from global and local features in a Linear Discriminant Analysis classifier, as well as feature fusion of all features in a Linear Discriminant Classifier

An evaluation of super-resolution for face recognition

We evaluate the performance of face recognition algorithms on images at various resolutions. Then we show to what extent super-resolution (SR) methods can improve the recognition performance when comparing low-resolution (LR) to high-resolution (HR) facial images. Our experiments use both synthetic data (from the FRGC v1.0 database) and surveillance images (from the SCface database). Three face recognition methods are used, namely Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Patterns (LBP). Two SR methods are evaluated. The first method learns the mapping between LR images and the corresponding HR images using a regression model. As a result, the reconstructed SR images are close to the HR images that belong to the same subject and far away from others. The second method compares LR and HR facial images without explicitly constructing SR images. It finds a coherent feature space where the correlation of LR and HR is maximum, and then compute the mapping from LR to HR in this feature space. The performance of the two SR methods are compared to that delivered by the standard face recognition without SR. The results show that LDA is mostly robust to resolution changes while LBP is not suitable for the recognition of LR images. SR methods improve the recognition accuracy when downsampled images are used and the first method provides better results than the second one. However, the improvement for realistic LR surveillance images remains limited

DESIGNING OF NEW PATTERN CLASSIFIER BASED ON MORPHOLOGICAL PARAMETER

Face and text recognition system should be able to automatically detect a face and text in any sample video or images. This involves extraction and analysis of its features. Pattern Classifier system recognizes face and text, regardless of lighting, ageing, occlusion, expression, illumination and pose. Morphological feature based on thresholding of image and gray level components analysis are used for linear discriminant analysis. These are than tested and compared for the template of face and text recognition of facial and textual images database. Present paper discusses designing of new pattern classifier based on morphological parameter. Present research used standard face 95 database, local database, and text databases. The performance of new pattern classifier based on morphological parameter is found to be 100%.Although performance of this classifier is highly dependent on the selection of parameters for thresholding and evaluation

Facial expression recognition with emotion-based feature fusion

© 2015 Asia-Pacific Signal and Information Processing Association. In this paper, we propose an emotion-based feature fusion method using the Discriminant-Analysis of Canonical Correlations (DCC) for facial expression recognition. There have been many image features or descriptors proposed for facial expression recognition. For the different features, they may be more accurate for the recognition of different expressions. In our proposed method, four effective descriptors for facial expression representation, namely Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Weber Local Descriptor (WLD), and Pyramid of Histogram of Oriented Gradients (PHOG), are considered. Supervised Locality Preserving Projection (SLPP) is applied to the respective features for dimensionality reduction and manifold learning. Experiments show that descriptors are also sensitive to the conditions of images, such as race, lighting, pose, etc. Thus, an adaptive descriptor selection algorithm is proposed, which determines the best two features for each expression class on a given training set. These two features are fused, so as to achieve a higher recognition rate for each expression. In our experiments, the JAFFE and BAUM-2 databases are used, and experiment results show that the descriptor selection step increases the recognition rate up to 2%

Texture Characteristic of Local Binary Pattern on Face Recognition with Probabilistic Linear Discriminant Analysis

Face recognition is an identification system that uses the characteristics of a person's face for processing. There is a feature in the face image so that it can be distinguished between one face and another face. One way to recognize face images is to analyze the texture of the face image. Texture analysis generally requires a feature extraction process. In different images, the characteristics will also differ. This characteristic will be the basis for the recognition of facial images. However, existing face recognition methods experience efficiency problems and rely heavily on the extraction of the right features. This study aims to study the texture characteristics of the extraction results using the Local Binary Pattern (LBP) method which is applied to deal with the introduction of Probabilistic Linear Discriminant Analysis (PLDA). The data used in this study are human face images from the AR Faces database, consisting of 136 objects (76 men and 60 women), each of which has 7 types of images Based on the results of testing shows the LBP method can produce the highest accuracy with a value of 95.53% in the introduction of PLDA

Face Analysis Using Row and Correlation Based Local Directional Pattern

Face analysis, which includes face recognition and facial expression recognition, has been attempted by many researchers and gave ideal solutions. The problem is still active and challenging due to an increase in the complexity of the problem viz. due to poor lighting, face occlusion, low-resolution images, etc. Local pattern descriptor methods introduced to overcome these critical issues and improve the recognition rate. These methods extract the discriminant information from the local features of the face image for recognition. In this paper, the local descriptor based two methods, namely row-based local directional pattern and correlation-based local directional pattern proposed by extending an existing descriptor -- local directional pattern (LDP). Further, the two feature vectors obtained by these methods concatenated to form a hybrid descriptor. Experimentation has carried out on benchmark databases and results infer that the proposed hybrid descriptor outperforms the other descriptors in face analysis