This paper presents a new preprocessing algorithm with local binary pattern for facial expression recognition. Firstly, we establish the skin color model to extract the face region, and then we use the cumulative proj...
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This paper presents a new preprocessing algorithm with local binary pattern for facial expression recognition. Firstly, we establish the skin color model to extract the face region, and then we use the cumulative projection to obtain the completely face region. Secondly, we rotate the inclined faces, normalize all the images, and remove the effect of light to get the required experimental samples. Finally, we use rotation invariance uniform local binary pattern operator to get the facial expression features, and then the support vector machine classifier is used for expression classification. The algorithm is implemented with Matlab and experimented on Indian male facial expression database. The proposed method obtained an accuracy of 72.75% which shows the effectiveness of the proposed algorithm.
Attribute reduction is one of the core research content of Rough sets theory. Many existing algorithms mainly are aimed at the reduction of consistency decision table, and very little work has been done for attribute ...
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A considerable amount of research work has been done for facial expression recognition using local or global feature extraction methods. Weber Local Descriptor (WLD), a simple and robust local image descriptor, is rec...
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A considerable amount of research work has been done for facial expression recognition using local or global feature extraction methods. Weber Local Descriptor (WLD), a simple and robust local image descriptor, is recently developed for local feature extraction. In facial expression recognition, the information contained in the local is important for the recognition result. The Histograms of Oriented Gradients (HOG) can well describe the local area information using gradient and orientation density distribution of the edge. In order to solve the lack of contour and shape information only by WLD features and to extract facial local features more efficiently, we propose a hybrid approach that combines the WLD with HOG features. We divide the images into blocks and weight each of them, then extract the two features and fuse them. At last, the weighted fused histograms are used to classify facial expressions by chi-square distance and the nearest neighbor method. The proposed method is applied on popular JAFFE and Cohn-Kanade facial expression databases and recognition rate is up to 93.97% and 95.86%. Compared with the Gabor Wavelet, LBP, and AAM and experimental results show that the proposed method achieves better performance for facial expression recognition.
Unconventional emergency is essentially a developing process that event state variable changes from steady to transient, then to a new steady state. This paper uses plant growth dynamics theory to study microcosmic dy...
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As a fundamental technique in data-driven (or example-based) methods, motion blending has been employed to produce new motion clip from two or more clips or introduced as a medium to improve the final result of other ...
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Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduce...
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Emotion recognition from speech is an important field of research in human computer interaction. In this letter the framework of Support Vector Machines (SVM) with Gaussian Mixture Model (GMM) supervector is introduced for emotional speech recognition. Because of the importance of variance in reflecting the distribution of speech, the normalized mean vectors potential to exploit the information from the variance are adopted to form the GMM supervector. Comparative experiments from five aspects are conducted to study their corresponding effect to system performance. The experiment results, which indicate that the influence of number of mixtures is strong as well as influence of duration is weak, provide basis for the train set selection of Universal Background Model (UBM).
The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper p...
The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.
Estimating taxonomic content constitutes a key problem in metagenomic sequencing data ***,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently avail...
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Estimating taxonomic content constitutes a key problem in metagenomic sequencing data ***,extracting such content from high-throughput data of next-generation sequencing is very time-consuming with the currently available ***,we present CloudLCA,a parallel LCA algorithm that significantly improves the efficiency of determining taxonomic composition in metagenomic data *** show that CloudLCA(1)has a running time nearly linear with the increase of dataset magnitude,(2)displays linear speedup as the number of processors grows,especially for large datasets,and(3)reaches a speed of nearly 215 million reads each minute on a cluster with ten thin *** comparison with MEGAN,a well-known metagenome analyzer,the speed of CloudLCA is up to 5 more times faster,and its peak memory usage is approximately 18.5%that of MEGAN,running on a fat *** can be run on one multiprocessor node or a *** is expected to be part of MEGAN to accelerate analyzing reads,with the same output generated as MEGAN,which can be import into MEGAN in a direct way to finish the following ***,CloudLCA is a universal solution for finding the lowest common ancestor,and it can be applied in other fields requiring an LCA algorithm.
MicroRNAs(miRNAs)are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression[1].A large number of miRNAs have been found to be involved in a broad spectrum of b...
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MicroRNAs(miRNAs)are a class of small non-coding RNAs that play important roles in post-transcriptional regulation of gene expression[1].A large number of miRNAs have been found to be involved in a broad spectrum of biological functions such as regulation of innate and adaptive immunity,cell differentiation and development as well as
In this paper, a RGB image encryption algorithm based on DNA encoding combined with chaotic map is proposed aiming at characteristics of RGB image. The algorithm firstly carries out DNA encoding for R, G, B components...
In this paper, a RGB image encryption algorithm based on DNA encoding combined with chaotic map is proposed aiming at characteristics of RGB image. The algorithm firstly carries out DNA encoding for R, G, B components of RGB image; then realizes the addition of R, G, B by DNA addition and carries out complement operation by using the DNA sequence matrix controlled by Logistic; three gray images are got after decoding; finally gets the encrypted RGB images by reconstructing R, G, B components which use image pixels disturbed by Logistic chaotic sequence. Simulation result shows that the proposed algorithm has a large secret key space and strong secret key sensitivity. Meanwhile, it can resist exhaustive attack, statistical attack, and thus it is suitable for RGB image encryption.
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