7,350 research outputs found
3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS
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
Signal processing and machine learning techniques for automatic image-based facial expression recognition
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
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