ebin Xu

Multispectral palmprint recognition using multiclass projection extreme learning machine and digital shearlet transform

A novel multispectral palmprint recognition method is proposed based on multiclass projection extreme learning machine (MPELM) and digital shearlet transform. Extreme learning machine (ELM) is a novel and efficient learning machine based on the generalized single-hidden-layer feedforward networks, which performs well in classification applications. Many researchers’ experimental results have shown the superiority of ELM with classical algorithm: support vector machine (SVM). To further improve the performance of multispectral palmprint recognition method, we propose a novel method based on MPELM in this paper. Firstly, all palmprint images are preprocessed by David Zhang’s method. Then, we use image fusion method based on fast digital shearlet transform to fuse the multispectral palmprint images. At last, we use the proposed MPELM classifier to determine the final multispectral palmprint classification. The experimental results demonstrate the superiority of multispectral fusion to each single spectrum, and the proposed MPELM-based method outperforms the SVM-based and ELM-based methods. The proposed method is also suitable for other biometric applications and gets to be work well.

Optimal Scheme of Retinal Image Enhancement using Curvelet Transform and Quantum Genetic Algorithm

A new optimal scheme based on curvelet transform is proposed for retinal image enhancement (RIE) using real-coded quantum genetic algorithm. Curvelet transform has better performance in representing edges than classical wavelet transform for its anisotropy and directional decomposition capabilities. For more precise reconstruction and better visualization, curvelet coefficients in corresponding subbands are modified by using a nonlinear enhancement mapping function. An automatic method is presented for selecting optimal parameter settings of the nonlinear mapping function via quantum genetic search strategy. The performance measures used in this paper provide some quantitative comparison among different RIE methods. The proposed method is tested on the DRIVE and STARE retinal databases and compared with some popular image enhancement methods. The experimental results demonstrate that proposed method can provide superior enhanced retinal image in terms of several image quantitative evaluation indexes.

Feature fusion of palmprint and face via tensor analysis and curvelet transform

In order to improve the recognition accuracy of the unimodal biometric system and to address the problem of the small samples recognition, a multimodal biometric recognition approach based on feature fusion level and curve tensor is proposed in this paper. The curve tensor approach is an extension of the tensor analysis method based on curvelet coefficients space. We use two kinds of biometrics: palmprint recognition and face recognition. All image features are extracted by using the curve tensor algorithm and then the normalized features are combined at the feature fusion level by using several fusion strategies. The k-nearest neighbour (KNN) classifier is used to determine the final biometric classification. The experimental results demonstrate that the proposed approach outperforms the unimodal solution and the proposed nearly Gaussian fusion (NGF) strategy has a better performance than other fusion rules.

Video analysis using spatiotemporal descriptor and kernel extreme learning machine for lip reading

Abstract. Lip-reading techniques have shown bright prospects for speech recognition under noisy environments and for hearing-impaired listeners. We aim to solve two important issues regarding lip reading: (1) how to extract discriminative lip motion features and (2) how to establish a classifier that can provide promising recognition accuracy for lip reading. For the first issue, a projection local spatiotemporal descriptor, which considers the lip appearance and motion information at the same time, is utilized to provide an efficient representation of a video sequence. For the second issue, a kernel extreme learning machine (KELM) based on the single-hidden-layer feedforward neural network is presented to distinguish all kinds of utterances. In general, this method has fast learning speed and great robustness to nonlinear data. Furthermore, quantum-behaved particle swarm optimization with binary encoding is introduced to select the appropriate feature subset and parameters for KELM training. Experiments conducted on the AVLetters and OuluVS databases show that the proposed lip-reading method achieves a superior recognition accuracy compared with two previous methods.

Multispectral image fusion for illumination-invariant palmprint recognition

Multispectral palmprint recognition has shown broad prospects for personal identification due to its high accuracy and great stability. In this paper, we develop a novel illumination-invariant multispectral palmprint recognition method. To combine the information from multiple spectral bands, an image-level fusion framework is completed based on a fast and adaptive bidimensional empirical mode decomposition (FABEMD) and a weighted Fisher criterion. The FABEMD technique decomposes the multispectral images into their bidimensional intrinsic mode functions (BIMFs), on which an illumination compensation operation is performed. The weighted Fisher criterion is to construct the fusion coefficients at the decomposition level, making the images be separated correctly in the fusion space. The image fusion framework has shown strong robustness against illumination variation. In addition, a tensor-based extreme learning machine (TELM) mechanism is presented for feature extraction and classification of two-dimensional (2D) images. In general, this method has fast learning speed and satisfying recognition accuracy. Comprehensive experiments conducted on the PolyU multispectral palmprint database illustrate that the proposed method can achieve favorable results. For the testing under ideal illumination, the recognition accuracy is as high as 99.93%, and the result is 99.50% when the lighting condition is unsatisfied.

A High Precision Quality Inspection System for Steel Bars Based on Machine Vision

Steel bars play an important role in modern construction projects and their quality enormously affects the safety of buildings. It is urgent to detect whether steel bars meet the specifications or not. However, the existing manual detection methods are costly, slow and offer poor precision. In order to solve these problems, a high precision quality inspection system for steel bars based on machine vision is developed. We propose two algorithms: the sub-pixel boundary location method (SPBLM) and fast stitch method (FSM). A total of five sensors, including a CMOS, a level sensor, a proximity switch, a voltage sensor, and a current sensor have been used to detect the device conditions and capture image or video. The device could capture abundant and high-definition images and video taken by a uniform and stable smartphone at the construction site. Then data could be processed in real-time on a smartphone. Furthermore, the detection results, including steel bar diameter, spacing, and quantity would be given by a practical APP. The system has a rather high accuracy (as low as 0.04 mm (absolute error) and 0.002% (relative error) of calculating diameter and spacing; zero error in counting numbers of steel bars) when doing inspection tasks, and three parameters can be detected at the same time. None of these features are available in existing systems and the device and method can be widely used to steel bar quality inspection at the construction site.

Classification of electroencephalogram signals using wavelet-CSP and projection extreme learning machine

Brain-computer interface (BCI) systems establish a direct communication channel from the brain to an output device. As the basis of BCIs, recognizing motor imagery activities poses a considerable challenge to signal processing due to the complex and non-stationary characteristics. This paper introduces an optimal and intelligent method for motor imagery BCIs. Because of the robustness to noise, wavelet packet decomposition and common spatial pattern (CSP) methods were implemented to reduce the dimensions of preprocessed signals. And a novel and efficient classifier projection extreme learning machine (PELM) was employed to recognize the labels of electroencephalogram signals. Experiments have been performed on the BCI Competition Dataset to demonstrate the superiority of wavelet-CSP in BCI and the outperformance of the PELM-based method. Results show that the average recognition rate of PELM approaches approximately 70%, while the optimal rate of other methods is 72%, whose training time and classification time are relatively longer as 11.00 ms and 11.66 ms, respectively, compared with 4.75 ms and 4.87 ms obtained by using the proposed BCI system.

Homeomorphic manifold analysis: Learning motion features of image sequence for lipreading

Lipreading techniques have shown bright prospects for speech recognition under noisy environments and for hearing-impaired listeners. In this paper, we discuss a feature extraction method based on the homeomorphic manifold analysis for lipreading. Given a set of image sequences, we think there is an underlying low dimensional unified manifold embedded in the visual space, and each image sequence can be considered as a homeomorphic manifold twisted or even self-intersected in this space. In order to extract the motion features, each sequence is embedded and aligned to the unified manifold, and a mapping matrix is learned from the aligned embedding. Then we adopt the two-dimensional linear discriminant analysis on these mapping matrices to achieve low dimensional features. The proposed method is tested on the OuluVS database, and simulation experiments have shown it can achieve quite satisfying results.