International Journal of Machine Intelligence and Sensory Signal Processing (8 papers in press)
Personal Authentication by Palmprint Using Contourlet
Transform and k-Nearest Neighbor Classifier
by Hedieh Sajedi, Bashir Ghasemi Moghaddam
Abstract: Biometric-based personal verification is a powerful security feature. Biometric systems are used the physiological and/or behavioral characteristics in each individual for verification. Palmprint is a reliable biometric that can be used for identity verification because it is stable and unique for every individual. In this paper, a new approach for personal authentication by palmprint using contourlet transform is presented. Contourlet transform is a multiscale and directional transform that captures image curvatures and smoothness with multidirectional decomposition capability, finely. Our proposed method includes three steps, preprocessing, feature extraction, and classification. In preprocessing stage, the central part of each palmprint is extracted. In feature extraction step, at first, contourlet transform is applied to the central part of palmprint and then features are extracted from created subbands. In this method for each image, 384 features are obtained. Finally, in classification step, Na
Keywords: Biometric; Palmprint Recognition; Contourlet Transform; K-Nearest Neighbor.
Designing Ensemble Learning Algorithms using Kernel Methods
by Fayao Liu
Abstract: Ensemble methods such as boosting combine multiple learners to obtain better prediction than could be obtained from any individual learner. Here we propose a principled framework for directly constructing ensemble learning methods from kernel methods. Unlike previous studies showing the equivalence between boosting and support vector machines (SVMs), which need a translation procedure, we show that it is possible to design boosting-like procedure to solve the SVM optimization problems. In other words, it is possible to design ensemble methods directly from SVM without any middle procedure. This finding not only enables us to design new ensemble learning methods directly from kernel methods, but also makes it possible to take advantage of those highly-optimized fast linear SVM solvers for ensemble learning. The resulted model is as effective as kernel methods while being as efficient as ensemble methods. We exemplify this framework for designing new binary and multi-class classification ensemble learning as well as a new quantile regression ensemble learning method. Experimental results demonstrate the flexibility and usefulness of the proposed framework.
Keywords: Kernel; Support vector machines; Ensemble learning; Column generation; Multi-class classification; Quantile regression.
Face Recognition against Pose Variations Using Multi-resolution Multiple Color Fusion
by Ling Li, Mustafa Alrjebi, Wanquan Liu
Abstract: As is shown with current research that effective utilization of color information can improve face recognition accuracy significantly. With only three color channels, there are plenty of researches for face recognition. Recently, MCF model based on more than three color components is proposed and shows more effective in face recognition. The 2D_MCF which is an extension of the MCF model in block-wise can achieve further improvement of recognition accuracy in comparison with MCF. However, all these above-mentioned approaches are developed for frontal face images and they very sensitive to pose variations. In this paper we propose a new color fusion approach called the Multi-resolution MCF (MMCF) model. Unlike the recently proposed MCF and 2D_MCF using the color images with same resolutions, this new model use the color information of images with different resolutions with an aim to find one optimal combination of different color combinations of images with different resolutions. More importantly, this new MMCF model can be used to face recognition against pose variations, especially when it is embedded with a deep learning network. To the best of our knowledge, this is the first time for color information to be used for face recognition with regard to pose variations. Extensive experiments on seven different databases show the superiority of the proposed model over the MCF, 2D_MCF and RGB color space, especially in the case of large pose variations, the corresponding improvements are significant. This research shed a light of future research on color face recognition against pose variations.
Keywords: Face recognition; color information utilization; deep learning.
Fuzzy Control for Discrete-Time Nonlinear Switched Systems with Improved Average Dwell Time
by Lei Liu, Hongyi Li, Yuxin Zhao
Abstract: This paper investigates the stabilization problem of discrete-time switched interval type-2 (IT2) fuzzy systems with improved average dwell time (ADT). Firstly, a novel stability condition is obtained with fewer constraints for a class of the switched system under the ADT switching. Secondly, the switched IT2 fuzzy modeling approach is applied to represent the underlying nonlinear discrete system. As the switching instant of controller lag behind the system is unavoidable in practice, a novel delayed IT2 fuzzy controller is designed to guarantee the closed-loop system to be asymptotically stable. Finally, the effectiveness of the proposed control scheme is illustrate on a numerical example.
Keywords: Average dwell time; Interval type-2 fuzzy Systems; Switched systems.
Node Estimate for Sparse Random Vector Functional-Link Networks
by Simone Scardapane, Aurelio Uncini
Abstract: A random vector functional-link (RVFL) network is a neural network composed of a randomized hidden layer and an adaptable output layer. Training such a network is reduced to a linear least-squares problem, which can be solved efficiently. Still, selecting a proper number of nodes in the hidden layer is a critical issue, since an improper choice can lead to either overfitting or underfitting for the problem at hand. Additionally, small sized RVFL networks are favored in situations where computational considerations are important. In the case of RVFL networks with a single output, unnecessary neurons can be removed adaptively with the use of sparse training algorithms such as Lasso, which are suboptimal for the case of multiple outputs. In this paper, we extend some prior ideas in order to devise a group sparse training algorithm which avoids the shortcomings of previous approaches. We validate our proposal on a large set of experimental benchmarks, and we analyze several state-of-the-art optimization techniques in order to solve the overall training problem. We show that the proposed approach can obtain an accuracy comparable to standard algorithms, while at the same time resulting in extremely sparse hidden layers.
Keywords: Pruning; Sparse learning; Random Weights; Neural Network.
Deep Learning in Digital Marketing: Brand Detection and Emotion Recognition
by Bernardete Ribeiro, Gonçalo Oliveira, Ana Laranjeira, Joel Arrais
Abstract: In this paper we explore deep learning in two case studies, brand detection and emotion recognition, that can have a prominent role in the marketing industry. The information that can be retrieved from brand detection technology and emotion recognition can increase competitiveness via the activity on digital social interactions between branding and the end-consumer. rnDeep learning is extremely good for visual feature extraction from images, audio signals or text which makes it very attractive to be used today. Given that specific datasets of logo images and emotion facial expression images can be easily processed by deep neural networks without the need to extract manually hand-crafted features, much higher performance than traditional methods can be obtained from these models. In particular, Convolutional Neural Networks (CNN) are becoming the current state of art in many image processing problems.rnIn the first case study, we build a graphic logo detection by using a Fast Region-based Convolutional Network (FRCN). This method looks for region proposals in the logo image, since the logo is often present in small sizes and partially occluded. This avoids a full search in the image and improves the object detection. Furthermore, instead of building the CNNs from the scratch which would be mostly prohibitive, transfer learning and data augmentation were used and have shown to excel previous approaches.rnIn the second case study, we present a robust way of facial emotions recognition by introducing an improved version of the classic CNN - LeNet-5. Despite the net simplicity, it was found to be better suited for the system constraints (dataset dimension, faces size and composition) by comparing its performance with deeper networks such as GoogleNet and AlexNet. Although a video tracker could successfully trace some of the facial expressions further improvements are still needed.Deep Learning; Brand Detection; Emotion Recognition
Keywords: Deep Learning; Convolutional Neural Networks; Brand Detection; Emotion Recognition.
Semi-Supervised Feature Selection with Sparse Representation for Hyperspectral Image Classification
by Yanyan Zhang, Shiguo Chen, Cailing Wang, Zhisong Pan, Daoqiang Zhang
Abstract: Dimensionality reduction is one of the most important steps for remotely sensed hyperspectral image classification. Feature selection as a kind of dimensionality reduction has attracted great attentions in the recent decades. In this paper, we proposed a novel feature selection method for hyperspectral image classification based on semi-supervised learning and sparsity score (or briefly called Semi-supervised Sparsity Score (Semi-SS)). In Semi-SS, the pairwise constraints instead of class labels are used as the supervision information. Furthermore, the features chosen by Semi-SS have the ability to reconstruct the original data and sparsity preserving. Experiments conducted on two famous hyperspectral data sets illustrate that the proposed algorithm is remarkably effective in comparison to the existing feature selection methods.
Keywords: Hyperspectral Image Classification; Semi-Supervised Feature Selection; Sparse Representation; Pairwise Constraints.
A Weighted Hybrid Training Algorithm of Neural Networks for Robust Data Regression
by Feilong Cao, Sifang Che, Jianwei Zhao
Abstract: Hybrid full memory Broyden-Fletcher-Golfarb-Shanno (BFGS) algorithm (HFM)rnis an effective technique for training feed-forward neural networks. It is superiority in training speed compared to classic second-order gradient methods. However, for training data with outliers, the performance of HFM is extremely affected. This paper addresses the robustness of HFM for regression with outliers, and proposes a new algorithm, named weighted regularization hybrid training algorithm (WRHFM), to solve this drawback. The main idea for this is to weight the error variable of regularization hybrid training algorithm (RHFM) by weighting factors, and to establish a robust model against outliers. The experiments are conducted on both function approximation and real-world data, which show that the proposed approach has the well robustness performance compared with classical back propagation algorithm (BP), supported vector machine (SVM), HFM, and RHFM in handling data with outliers.
Keywords: Feed-forward neural networks; hybrid full memory; alternative
optimization; robust data regression.