7,735 research outputs found

    3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

    Get PDF
    Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

    Spatio-temporal framework on facial expression recognition.

    Get PDF
    This thesis presents an investigation into two topics that are important in facial expression recognition: how to employ the dynamic information from facial expression image sequences and how to efficiently extract context and other relevant information of different facial regions. This involves the development of spatio-temporal frameworks for recognising facial expression. The thesis proposed three novel frameworks for recognising facial expression. The first framework uses sparse representation to extract features from patches of a face to improve the recognition performance, where part-based methods which are robust to image alignment are applied. In addition, the use of sparse representation reduces the dimensionality of features, and improves the semantic meaning and represents a face image more efficiently. Since a facial expression involves a dynamic process, and the process contains information that describes a facial expression more effectively, it is important to capture such dynamic information so as to recognise facial expressions over the entire video sequence. Thus, the second framework uses two types of dynamic information to enhance the recognition: a novel spatio-temporal descriptor based on PHOG (pyramid histogram of gradient) to represent changes in facial shape, and dense optical flow to estimate the movement (displacement) of facial landmarks. The framework views an image sequence as a spatio-temporal volume, and uses temporal information to represent the dynamic movement of facial landmarks associated with a facial expression. Specifically, spatial based descriptor representing spatial local shape is extended to spatio-temporal domain to capture the changes in local shape of facial sub-regions in the temporal dimension to give 3D facial component sub-regions of forehead, mouth, eyebrow and nose. The descriptor of optical flow is also employed to extract the information of temporal. The fusion of these two descriptors enhance the dynamic information and achieves better performance than the individual descriptors. The third framework also focuses on analysing the dynamics of facial expression sequences to represent spatial-temporal dynamic information (i.e., velocity). Two types of features are generated: a spatio-temporal shape representation to enhance the local spatial and dynamic information, and a dynamic appearance representation. In addition, an entropy-based method is introduced to provide spatial relationship of different parts of a face by computing the entropy value of different sub-regions of a face

    Signal processing and machine learning techniques for automatic image-based facial expression recognition

    Get PDF
    PhD ThesisIn this thesis novel signal processing and machine learning techniques are proposed and evaluated for automatic image-based facial expression recognition, which are aimed to progress towards real world operation. A thorough evaluation of the performance of certain image-based expression recognition techniques is performed using a posed database and for the rst time three progressively more challenging spontaneous databases. These methods exploit the principles of sparse representation theory with identity-independent expression recognition using di erence images. The second contribution exploits a low complexity method to extract geometric features from facial expression images. The misalignment problem of the training images is solved and the performance of both geometric and appearance features is assessed on the same three spontaneous databases. A deep network framework that contains auto-encoders is used to form an improved classi er. The nal work focuses upon enhancing the expression recognition performance by the selection and fusion of di erent types of features comprising geometric features and two sorts of appearance features. This provides a rich feature vector by which the best representation of the spontaneous facial features is obtained. Subsequently, the computational complexity is reduced by maintaining important location information by concentrating on the crucial roles of the facial regions as the basic processing instead of the entire face, where the local binary patterns and local phase quantization features are extracted automatically by means of detecting two important regions of the face. Next, an automatic method for splitting the training e ort of the initial network into several networks and multi-classi ers namely a surface network and bottom network are used to solve the problem and to enhance the performance. All methods are evaluated in a MATLAB framework and confusion matrices and average facial expression recognition accuracy are used as the performance metrics.Ministry of Higher Education and Scienti c Research in Iraq (MOHESR
    • …
    corecore