https://ibug.doc.ic.ac.uk/media/uploads/documents/sandbach2012survey.pdf

Static and dynamic 3D facial expression recognition: A comprehensive survey

Stereoscopic image quality assessment method based on binocular combination saliency model

Tool wear monitoring using an online, automatic and low cost system based on local texture

Face recognition has been a fast growing, challenging and interesting area in real time applications. A large number of face recognition algorithms have been developed in last decades. In this paper an attempt is made to review a wide range of methods used for face recognition comprehensively. This include PCA, LDA, ICA, SVM, Gabor wavelet soft computing tool like ANN for recognition and various hybrid combination of this techniques. This review investigates all these methods with parameters that challenges face recognition like illumination, pose variation, facial expressions. Index Terms

A Review Paper on Face Recognition Techniques

Face recognition is being widely accepted as a biometric technique because of its non-intrusive nature. Despite extensive research on 2-D face recognition, it suffers from poor recognition rate due to pose, illumination, expression, ageing, makeup variations and occlusions. In recent years, the research focus has shifted toward face recognition using 3-D facial surface and shape which represent more discriminating features by the virtue of increased dimensionality. This paper presents an extensive survey of recent 3-D face recognition techniques in terms of feature detection, classifiers as well as published algorithms that address expression and occlusion variation challenges followed by our critical comments on the published work. It also summarizes remarkable 3-D face databases and their features used for performance evaluation. Finally we suggest vital steps of a robust 3-D face recognition system based on the surveyed work and identify a few possible directions for research in this area.

3-D face recognition: features, databases, algorithms and challenges

This paper describes a 3D facial expression recognition approach based on distance and angle features, which can be got from the localized facial feature points. The probabilistic Neutral Network (PNN) architecture is used to classify the facial expressions based on BU-3DFE database. This paper adds the facial feature vectors with the information of slopes and the angles as the feature vectors got from the facial feature points, not only the distance information mentioned in the previous work. Thus it receives a better performance with an average recognition rate of 90.2%.

3D Facial expression recognition based on basic geometric features

Fully automatic 3D facial expression recognition using polytypic multi-block local binary patterns

In this paper, a novel framework for 3D face recognition based on depth information, is proposed. The core of our framework is Spectral Regression Kernel Discriminate Analysis (SRKDA), a method for utilizing a reproducing kernel Hubert space (RKHS) into which data points are mapped. In order to overcome facial expression variation, we first utilize curvature information projected onto the moving least-squares (MLS) surface to segment a face rigid area, which is insensitive to expression variation. Then we make use of SRKDA to extract discrimination features for a depth image obtained by use of a 3D face mesh model, thus avoiding an eigen-decomposition of a kernel matrix. This effectively merges 3D facial shape information; then a nearest neighbor classifier is used for recognition. A non-linear kernel trick solves the high dimensional small sample size problem, and enhances feature extraction from the local non-linear structures of a face. Our experiments, using the CASIA 3D face database, show our framework performs more effectively and efficiently than many commonly used methods. SRKDA decreased the complexity from cubic-time to quadratic-time resulting in a very significant reduction in computation time. In addition, recognition accuracy, based on face rigid areas, improved accuracy significantly when compared to accuracy before segmentation.

Xiaoli Li

Papers

3D Facial expression recognition based on basic geometric features

Fully automatic 3D facial expression recognition using polytypic multi-block local binary patterns

Efficient Kernel Discriminate Spectral Regression for 3D face recognition

Multiple strategies to enhance automatic 3D facial expression recognition

A remarkable standard for estimating the performance of 3D facial expression features