Chunxiao Fan

Stacked PCA Network (SPCANet): An effective deep learning for face recognition

High-level features can represent the semantics of the original data and it is a plausible way to avoid the problem of hand-crafted features for face recognition. This paper proposes an effective deep learning framework by stacking multiple output features that learned through each stage of the Convolutional Neural Network (CNN). Different from the traditional deep learning network, we use Principal Component Analysis (PCA) to get the filter kernels of convolutional layer, which is name as Stacked PCA Network (SPCANet). Our SPCANet model follows the basic architecture of the CNN, which comprises three layers in each stage: convolutional filter layer, nonlinear processing layer and feature pooling layer. Firstly, in the convolutional filter layer of our model, PCA instead of stochastic gradient descent (SGD) is employed to learn filter kernels, and the output of all cascaded convolutional filter layers is used as the input of nonlinear processing layer. Secondly, the following nonlinear processing layer is also simplified. We use hashing method for nonlinear processing. Thirdly, the block based histograms instead of max-pooling technique are employed in the feature pooling layer. In the last output layer, the output of each stage is stacked together as one final feature output of our model. Extensive experiments conducted on many different face recognition scenarios demonstrate the effectiveness of our proposed approach.

Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition

With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

Multiple scales combined principle component analysis deep learning network for face recognition

Abstract. It is well known that higher level features can represent the abstract semantics of original data. We propose a multiple scales combined deep learning network to learn a set of high-level feature representations through each stage of convolutional neural network for face recognition, which is named as multiscaled principle component analysis (PCA) Network (MS-PCANet). There are two main differences between our model and the traditional deep learning network. On the one hand, we get the prefixed filter kernels by learning the principal component of images’ patches using PCA, nonlinearly process the convolutional results by using simple binary hashing, and pool them using spatial pyramid pooling method. On the other hand, in our model, the output features of several stages are fed to the classifier. The purpose of combining feature representations from multiple stages is to provide multiscaled features to the classifier, since the features in the latter stage are more global and invariant than those in the early stage. Therefore, our MS-PCANet feature compactly encodes both holistic abstract information and local specific information. Extensive experimental results show our MS-PCANet model can efficiently extract high-level feature presentations and outperform state-of-the-art face/expression recognition methods on multiple modalities benchmark face-related datasets.

Learning iterative quantization binary codes for face recognition

Binary feature descriptors have been widely used in computer vision field due to their excellent discriminative power and strong robustness, and local binary patterns (LBP) and its variations have proven that they are effective face descriptors. However, the forms of such binary feature descriptors are predefined in the hand-crafted way, which requires strong domain knowledge to design them. In this paper, we propose a simple and efficient iterative quantization binary codes (IQBC) feature learning method to learn a discriminative binary face descriptor in the data-driven way. Firstly, similar to traditional LBP method, we extract patch-wise pixel difference vectors (PDVs) by computing and concatenating the difference between center patch and its neighboring patches. Then, inspired by multi-class spectral clustering and the orthogonal Procrustes problem, which both are widely used in image retrieval field, we learn an optimized rotation to minimize the quantization error of mapping data to the vertices of a zero-centered binary hypercube by using iterative quantization scheme. In other words, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors. And our IQBC can be used with unsupervised data embedding method such as principle component analysis (PCA) and supervised data embedding method such as canonical correlation analysis (CCA), namely IQBC-PCA and IQBC-CCA. Lastly, we cluster and pool these projected binary codes into a histogram-based feature that describes the co-occurrence of binary codes. And we consider the histogram-based feature as our final feature representation for each face image. We investigate the performance of our IQBC-PCA and IQBC-CCA on FERET, CAS-PEAL-R1, LFW and PaSC databases. Extensive experimental results demonstrate that our IQBC descriptor outperforms other state-of-the-art face descriptors.

Learning spherical hashing based binary codes for face recognition

Local feature descriptor has been widely used in computer vision field due to their excellent discriminative power and strong robustness. However, the forms of such local descriptors are predefined in the hand-crafted way, which requires strong domain knowledge to design them. In this paper, we propose a simple and efficient Spherical Hashing based Binary Codes (SHBC) feature learning method to learn a discriminative and robust binary face descriptor in the data-driven way. Firstly, we extract patch-wise pixel difference vectors (PDVs) by computing the difference between center patch and its neighboring patches. Then, inspired by the fact that hypersphere provide much stronger power in defining a tighter closed region in the original data space than hyperplane, we learn a hypersphere-based hashing function to map these PDVs into low-dimensional binary codes by an efficient iterative optimization process, which achieves both balanced bits partitioning of data points and independence between hashing functions. In order to better capture the semantic information of the dataset, our SHBC also can be used with supervised data embedding method, such as Canonical Correlation Analysis (CCA), namely Supervised-SHBC (S-SHBC). Lastly, we cluster and pool these learned binary codes into a histogram-based feature that describes the co-occurrence of binary codes. And we consider the histogram-based feature as our final feature representation for each face image. We investigate the performance of our SHBC and S-SHBC on FERET, CAS-PEAL-R1, LFW and PaSC databases. Extensive experimental results demonstrate that our SHBC descriptor outperforms other state-of-the-art face descriptors.

SRDANet: An Efficient Deep Learning Algorithm for Face Analysis

In this work, we take advantage of the superiority of Spectral Graph Theory in classification application and propose a novel deep learning framework for face analysis which is called Spectral Regression Discriminant Analysis Network SRDANet. Our SRDANet model shares the same basic architecture of Convolutional Neural Network CNN, which comprises three basic components: convolutional filter layer, nonlinear processing layer and feature pooling layer. While it is different from traditional deep learning network that in our convolutional layer, we extract the leading eigenvectors from patches in facial image which are used as filter kernels instead of randomly initializing kernels and update them by stochastic gradient descent SGD. And the output of all cascaded convolutional filter layers is used as the input of nonlinear processing layer. In the following nonlinear processing layer, we use hashing method for nonlinear processing. In feature pooling layer, the block-based histograms are employed to pooling output features instead of max-pooling technique. At last, the output of feature pooling layer is considered as one final feature output of our model. Different from the previous single-task research for face analysis, our proposed approach demonstrates an excellent performance in face recognition and expression recognition with 2D/3D facial images simultaneously. Extensive experiments conducted on many different face analysis databases demonstrate the efficiency of our proposed SRDANet model. Databases such as Extended Yale B, PIE, ORL are used for 2D face recognition, FRGC v2 is used for 3D face recognition and BU-3DFE is used for 3D expression recognition.

3D human behavior recognition based on spatiotemporal texture features

Nowadays, more and more activity recognition algorithms begin to improve recognition performance by combining the RGB and depth information. Although, the space-time volumes (STV) algorithm and the space-time local features algorithm can combine the RGB and depth information effectively, they also have their own defects. Such as they need expensive computational cost and they are not suitable for modeling nonperiodic activity. In this paper, we propose a novel algorithm for three dimensional human activity recognition that combines spatial-domain local texture features and spatio-temporal local texture features. On the one hand, in order to extract spatial local texture features, we mix the RGB and depth image sequence which have been applied with ViBe (Visual Background extractor) and binarization operator. Then we obtain the RGB-MOHBBI and depth-MOBHBI respectively and perform intersect operation on them. Afterwards, we extract LBP feature from the mixed MOHBBI to describe spatial domain feature. On the other hand, we follow the same background subtraction and binarization method to process the RGB and depth image sequences and get the spatial-temporal local texture features. And then, we project the three dimensional image volume on plane X-T and plane Y-T to get the spatio-temporal behavior volume change image to which we apply LBP operator to extract features that can represent human activity feature in spatio-temporal domain. At last, we combine the two local features that are extracted by LBP algorithm as one integrated feature of our model final output. Extensive experiments are conducted on the BUPT Arm Activity Dataset and the BUPT Arm And Finger Activity Dataset. The experimental results demonstrate the algorithm we proposed in this paper can make up for the deficiency of traditional activity recognition algorithms effectively and provide excellent experiment results on different databases of various complexities.

Improving Deep Neural Network with Multiple Parametric Exponential Linear Units

Activation function is crucial to the recent successes of deep neural networks. In this paper, we first propose a new activation function, Multiple Parametric Exponential Linear Units (MPELU), aiming to generalize and unify the rectified and exponential linear units. As the generalized form, MPELU shares the advantages of Parametric Rectified Linear Unit (PReLU) and Exponential Linear Unit (ELU), leading to better classification performance and convergence property. In addition, weight initialization is very important to train very deep networks. The existing methods laid a solid foundation for networks using rectified linear units but not for exponential linear units. This paper complements the current theory and extends it to the wider range. Specifically, we put forward a way of initialization, enabling training of very deep networks using exponential linear units. Experiments demonstrate that the proposed initialization not only helps the training process but leads to better generalization performance. Finally, utilizing the proposed activation function and initialization, we present a deep MPELU residual architecture that achieves state-of-the-art performance on the CIFAR-10/100 datasets. The code is available at this https URL.

Sparse projections matrix binary descriptors for face recognition

Abstract In recent years, the binary feature descriptor has achieved great success in face recognition (FR) field, such as local binary pattern (LBP). It is well known that the high-dimensional feature representations can contain more discriminative information, therefore, it is natural for us to construct the high-dimensional binary feature for FR task. However, the high-dimensional representations would lead to high computational cost and overfitting. Therefore, an effective sparsity regularizer is necessary. In this paper, we introduce the sparsity constraint into the objective function of general binary codes learning framework, so that the problem of high computational cost and overfitting can be somehow solved. There are three main requirements in our objective function: First, we require that the high-dimensional binary codes have the minimized quantization loss compared with centered original data. Second, we require the projection matrices are sparse, so that the projection process would not take lots of computational resource even faced with high-dimensional original data. Third, for a mapping (hashing) function, the bit-independence and bit-balance are two excellent properties for generating discriminative binary codes. We also empirically show that the high-dimensional binary codes can obtain more discriminative ability by pooling process with an unsupervised clustering method. Therefore, a discriminative and low-cost Sparse Projection Matrix Binary Descriptors (SPMBD) is learned by the data-driven way. Extensive experimental results on four public datasets show that our SPMBD descriptor outperforms other existing face recognition algorithms and demonstrate the effectiveness and robustness of the proposed methods.

PCANet-II: When PCANet Meets the Second Order Pooling

PCANet, as one noticeable shallow network, employs the histogram representation for feature pooling. However, there are three main problems about this kind of pooling method. First, the histogram-based pooling method binarizes the feature maps and leads to inevitable discriminative information loss. Second, it is difficult to effectively combine other visual cues into a compact representation, because the simple concatenation of various visual cues leads to feature representation inefficiency. Third, the dimensionality of histogram-based output grows exponentially with the number of feature maps used. In order to overcome these problems, we propose a novel shallow network model, named as PCANet-II. Compared with the histogram-based output, the second order pooling not only provides more discriminative information by preserving both the magnitude and sign of convolutional responses, but also dramatically reduces the size of output features. Thus we combine the second order statistical pooling method with the shallow network, i.e., PCANet. Moreover, it is easy to combine other discriminative and robust cues by using the second order pooling. So we introduce the binary feature difference encoding scheme into our PCANet-II to further improve robustness. Experiments demonstrate the effectiveness and robustness of our proposed PCANet-II method.