A novel decorrelated neural network ensemble algorithm for face recognition

Dai, Kankan; Zhao, Jianwei; Cao, Feilong

doi:10.1016/j.knosys.2015.09.002

Cited by 19 publications

(8 citation statements)

References 32 publications

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“…3 Results and discussion 3.1 Collection and preprocessing of point cloud data of rice seed surface As can be seen in the marked areas in Figure 7, the noise points on the rice seed surface were filtered, the missing parts were improved, and a few points represented the original morphological characteristics of the rice seeds. Thus, the preprocessing of the collected point cloud data of the rice seeds effectively solved the problems of noise points, voids, and data redundancy, obtaining more precise and smooth point cloud data.…”

Section: Classification and Identification Of Rice Seeds Using Bp Neumentioning

confidence: 99%

“…Its input layer receives input signal, which is then processed and transmitted to the hidden layer, and after that, transmitted to the output layer. Due to the strong ability of nonlinear mapping, self-learning, adaptive capacity, generalization ability, and fault tolerance, the BP neural networks are the most widely used neural network models in face recognition [3,4] , license plate recognition [5] , palmprint recognition [6] , etc.…”

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Rice seeds identification based on back propagation neural network model

Feng

Zhang

et al. 2019

International Journal of Agricultural and Biological Engineering

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Rice quality directly affects the final rice yield. In order to achieve rapid, non-destructive testing of rice seeds, this paper combines the three-dimensional laser scanning technology and back propagation (BP) neural network algorithm to build a rice seeds identification platform. The information on rice seed surface is collected from four angles and processed using Geomagic Studio software. Based on the noise filtering, smoothing of the point cloud, vulnerability repair, and downsampling, the three-dimensional (3D) morphological characteristics of a rice seed surface, and the projection features of the main plane cross-section are obtained through the calculation of the features. The experiments were performed on five rice varieties, including Da Hua aromatic glutinous, Hong Shi Ⅰ, Tian You VIII, Xin Dao X, and Yu Jing VI. The resulting input vector consisted respectively of: (1) nine 3D morphological surface features, (2) nine projection features of the main cross-section plane of rice, and (3) all of the above features. The results showed that for an input vector consisting of nine surface 3D morphological features, the recognition rate of the five rice varieties was 95%, 96%, 87%, 93%, and 89%, respectively; for an input vector consisting of nine projection features of the main cross-section plane of rice seeds, the recognition rate was 96%, 96%, 90%, 92%, and 89%, respectively; and lastly, for an input vector consisting of all the features, the highest recognition rate of 96%, 97%, 91%, 94%, and 90%, respectively, was achieved. The analysis showed that rice varieties could be identified by using 3D laser scanning. Therefore, the proposed method can improve the accuracy of rice varieties identification.

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Section: Classification and Identification Of Rice Seeds Using Bp Neumentioning

confidence: 99%

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confidence: 99%

Rice seeds identification based on back propagation neural network model

Feng

Zhang

et al. 2019

International Journal of Agricultural and Biological Engineering

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“…Ensemble learning has its composition being made of different classifiers. Ensemble learning algorithms have also been employed in face recognition [10][11][12][13][14]. In trying to create an efficient and effective system, there is a need for efficient computing methods and techniques that have increased classification accuracy [12].…”

Section: Research Motivationsmentioning

confidence: 99%

“…Zhao et al [8] offered a super-resolution reconstruction method by adaptive multi-dictionary learning, and compared it with the traditional global dictionary learning, which uses less time on dictionary training and image rebuilding to a high level. Dai et al [10] used neural networks with random weights to apply such learning structures to the face recognition field.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

2018

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Biometry based authentication and recognition have attracted greater attention due to numerous applications for security-conscious societies, since biometrics brings accurate and consistent identification. Face biometry possesses the merits of low intrusiveness and high precision. Despite the presence of several biometric methods, like iris scan, fingerprints, and hand geometry, the most effective and broadly utilized method is face recognition, because it is reasonable, natural, and non-intrusive. Face recognition is a part of the pattern recognition that is applied for identifying or authenticating a person that is extracted from a digital image or a video automatically. Moreover, current innovations in big data analysis, cloud computing, social networks, and machine learning have allowed for a straightforward understanding of how different challenging issues in face recognition might be solved. Effective face recognition in the enormous data concept is a crucial and challenging task. This study develops an intelligent face recognition framework that recognizes faces through efficient ensemble learning techniques, which are Random Subspace and Voting, in order to improve the performance of biometric systems. Furthermore, several methods including skin color detection, histogram feature extraction, and ensemble learner-based face recognition are presented. The proposed framework, which has a symmetric structure, is found to have high potential for biometrics. Hence, the proposed framework utilizing histogram feature extraction with Random Subspace and Voting ensemble learners have presented their superiority over two different databases as compared with state-of-art face recognition. This proposed method has reached an accuracy of 99.25% with random forest, combined with both ensemble learners on the FERET face database.

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“…Classification, which is a supervised learning task, is relevant in the context of data mining and machine learning. It has many distinct applications for different areas, such as energy systems, biology, medicine, facial recognition, image processing, and object detection 1–6 …”

Section: Introductionmentioning

confidence: 99%

Deep semi‐supervised classification based in deep clustering and cross‐entropy

et al. 2021

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Self‐labeled techniques, a semi‐supervised classification paradigm (SSC), are highly effective in alleviating the scarcity of labeled data used in classification tasks through an iterative process of self‐training. This problem was addressed by several approaches with different assumptions about the features of the input data, examples of these approaches being self‐training, co‐training, STRED, among others. This paper presents a framework for data self‐labeling based on deep autoencoder combined with a self‐labeled technique that takes into consideration cross‐entropy. The model uses the Encoder to reduce the dimensionality of the input that is submitted to a labeling layer. The weights of this layer are adjusted through the learning from a clustering performed in the Z space, which is the reduced dimensionality space. Results showed that the proposed method obtained competitive performance in relation to classic methods that are found in the literature.

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A novel decorrelated neural network ensemble algorithm for face recognition

Cited by 19 publications

References 32 publications

Rice seeds identification based on back propagation neural network model

Rice seeds identification based on back propagation neural network model

Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition

Deep semi‐supervised classification based in deep clustering and cross‐entropy

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