Facial expression recognition based on Gabor features and sparse representation

The theory of compressive sensing applies the sparse representation to the extraction of useful information from signals and brings a breakthrough to the theory of signal sampling. Based on compressive sensing, sparse representation-based classification (SRC) is proposed. SRC uses the compressibility of the image data to represent the facial image sparsely and could solve the problems of both massive calculation and information loss in dealing with signals. SRC does not, however, deal with the effects of variable illumination, posture and incomplete face image, which could result in severe performance degradation. This paper studies the differences between SRC recognition and human recognition. We find that there is an obvious disadvantage in the SRC algorithm, and it will significantly affect the face recognition performance in actual environment, especially for the variable illumination, posture and incomplete face image. To overcome the disadvantage of SRC algorithm, we propose an SRC-based twice face recognition algorithm named T_SRC. T_SRC uses bidirectional PCA, linear discriminant analysis and GradientFace to execute multichannel analysis, which could extract more "holistic/configural" face features in actual environment than by using SRC algorithm directly. Based on the multichannel analysis, we identify the test image by SRC firstly. Then, by analyzing the residual, this algorithm could decide whether the twice recognition is needed. If the twice recognition is needed, T_SRC extracts the facial details ("featural" face features) by the improved Harris point and Gabor filter detector. We suppose that the facial details are more stable than the whole face in actual environment, and later experiments verify our assumption. At last, this algorithm identifies the class of the test image by SRC again. The results of the experiments prove that the T_SRC algorithm has better recognition rate than SRC.

Aiming at the limitation of single feature classification, this paper proposes a facial expression recognition algorithm based on feature fusion and sparse representation residual fusion. The proposed method is composed of three components. Firstly, a feature fusion method based on local binary quantization (LBP) and local phase quantization (LPQ) is proposed. The LBP/LPQ features are an expression feature with image space and frequency domain. Second, Curvelet transform is used to extract the global features of facial expression, which is a very effective multi-scale, band-pass and direction analysis tool. Finally, the LBP/LPQ features and the Curvelet feature are classified by the sparse representation based classification (SRC) with stronger discrimination and robustness respectively. According to the sparse residuals of LBP/LPQ+SRC and Curvelet+SRC, a classification method of residual fusion is proposed. Experiments on JAFFE database and Cohn-Kanade database show that the proposed method effectively improves the ability of facial expression recognition and achieves a high recognition rate.

Sparse representation based classification (SRC) has gained great success in image recognition. In this paper, we propose a generalized CRC method, essentially a SRC using Euler sparse representation, for image classification. To be specific, our CRC first maps the image into a complex space by Euler representation with a negligible effect for outliers and illumination, and then performs a complex SRC method. We also present a fast and efficient algorithm to solve Euler SRC subproblems. Extensive experimental results illustrate that our method outperforms traditional SRC methods and achieves better performance for image classification.

Robust automatic facial expression detection method based on sparse representation plus LBP map

Single-sample face recognition is one of the most challenging problems in face recognition. We propose a novel algorithm to address this problem based on a sparse representation based classification (SRC) framework. The new algorithm is robust to image misalignment and pixel corruption, and is able to reduce required gallery images to one sample per class. To compensate for the missing illumination information traditionally provided by multiple gallery images, a sparse illumination learning and transfer (SILT) technique is introduced. The illumination in SILT is learned by fitting illumination examples of auxiliary face images from one or more additional subjects with a sparsely-used illumination dictionary. By enforcing a sparse representation of the query image in the illumination dictionary, the SILT can effectively recover and transfer the illumination and pose information from the alignment stage to the recognition stage. Our extensive experiments have demonstrated that the new algorithms significantly outperform the state of the art in the single-sample regime and with less restrictions. In particular, the single-sample face alignment accuracy is comparable to that of the well-known Deformable SRC algorithm using multiple gallery images per class. Furthermore, the face recognition accuracy exceeds those of the SRC and Extended SRC algorithms using hand labeled alignment initialization.

We propose a fast kernel sparse representation based classification (SRC) for undersampling problem, i.e., each class has very few training samples, in face recognition. The proposed algorithm exploits a nonlinear mapping to map the data from the original input space into a high-dimensional feature space. Then, it performs very fast sparse representation and classification of samples in this space. Similar to the typical SRC methods, the proposed approach is based on the L1 norm minimization, whose direct solution can be very time-consuming. In order to improve the computational efficiency, our method uses the coordinate descent method in the feature space, which can avoid directly solving the L1 norm minimization problem, and significantly expedites the computational procedure. Compared with other SRC methods based on the L1 norm minimization, our proposed method achieves very high computational efficiency, without significantly degrading the classification performance. Several experiments on popular face databases demonstrate that our method is a promising efficient kernel SRC based method.

Sparse representation based classification (SRC) and collaborative representation based classification (CRC) are two well-known methods in representation-based classification for face recognition. SRC emphasizes on the role of sparsity in coding, while CRC obtains an enhanced sparse representation with collaborative contributions from all classes. Previous studies show that sparsity of representation coefficients of SRC and CRC is significant for robust classification that may amplify the nonlocal contribution from other classes. However, the parameters are closely associated with the sparsity and it is very difficult to solve the optimal solutions. This paper proposed a novel representation-based image classification method that integrates SRC with CRC by a multiplication operation on the solutions to obtain enhanced sparse representation. We conducted experiments on several benchmark face databases and the experimental results showed that our algorithm produces higher accuracy on classification than both SRC and CRC.

In this paper, we proposed a novel algorithm for Facial Expression Recognition (FER) which was based on fusion of gabor texture features and Local Phase Quantization (LPQ). Firstly, the LPQ feature and gabor texture feature were respectively extracted from every expression image. LPQ features are histograms of LPQ transform. Five scales and eight orientations of gabor wavelet filters are used to extract gabor texture features and adaboost algorithm is used to select gabor features. Then we obtain two expression recognition results on both expression features by Sparse Representation-based Classification (SRC) method. Finally, the final expression recognition was performed by fusion of residuals of two SRC algorithms. The experiment results on Japanese Female Facial Expression (JAFFE) database demonstrated that the new algorithm was better than the original two algorithms, and this algorithm had a much higher recognition rate than the traditional algorithm.

Joint discriminative and shared dictionary learning with dictionary extension strategy for bearing fault classification

Mapping cotton cultivated area combining remote sensing with a fused representation-based classification algorithm

We propose a generalized Sparse Representation-based Classification (SRC) algorithm for open set recognition where not all classes presented during testing are known during training. The SRC algorithm uses class reconstruction errors for classification. As most of the discriminative information for open set recognition is hidden in the tail part of the matched and sum of non-matched reconstruction error distributions, we model the tail of those two error distributions using the statistical Extreme Value Theory (EVT). Then we simplify the open set recognition problem into a set of hypothesis testing problems. The confidence scores corresponding to the tail distributions of a novel test sample are then fused to determine its identity. The effectiveness of the proposed method is demonstrated using four publicly available image and object classification datasets and it is shown that this method can perform significantly better than many competitive open set recognition algorithms.

Gabor feature based robust representation and classification for face recognition with Gabor occlusion dictionary

This paper presents a method to perform sparse representation based classification (SRC) in a more accurate and efficient way. In this method, training data is first mapped into different feature spaces and multiple dictionaries are built by utilizing a Fisher discriminative based method. These dictionaries can be considered as efficient representations of the data which are then used in a multimodal SRC framework to classify test samples. In comparison to the original SRC method where only one modality of training space is utilized, the proposed method classifies test samples in a more accurate and efficient way. Experimental results from two different face datasets show that the proposed multimodal method has higher recognition rate compared to single-modality SRC based methods. The accuracy of the proposed method is also compared to other multi-modality classifiers and the results confirm that higher recognition rates are achieved in comparison with other common classification algorithms.

In recent years, sparse representation and dictionary learning methods have produced state-of-the-art results in many biometric recognition problems such as face, gait and iris recognition. However, when sparse representation-based classification methods are confronted with situations where the training data has different distribution than the test data, their performance degrades significantly. In this paper, we propose a general sparse representation-based classification method that learns projections of data in a space where the sparsity of data is maintained. We propose an efficient iterative procedure for solving the proposed optimization problem. One of the key features of the proposed method is that it is computationally efficient as the learning is done in the lower-dimensional space. Various experiments on mobile active authentication datasets consisting of face and screen touch gestures show that our method is able to capture the meaningful structure of data and can perform significantly better than many competitive domain adaptation algorithms.

We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoencoder along with a fully connected layer. The role of the autoencoder network is to learn robust deep features for classification. On the other hand, the fully connected layer, which is placed in between the encoder and the decoder networks, is responsible for finding the sparse representation. The estimated sparse codes are then used for classification. Various experiments on three different datasets show that the proposed network leads to sparse representations that give better classification results than state-of-the-art SRC methods. The source code is available at: github.com/mahdiabavisani/DSRC.