A Hybrid Method of Face Feature Extraction, Classification Based on MLBP and Layered-Recurrent Network

Abstract

Face feature extraction and classification is an attracting research area for its various applications. This paper proposes a hybrid technique based on modified local binary pattern (MLBP) and Layered-Recurrent neural network (L-RNN) to recognize the human faces. The proposed MLBP algorithm reduces the dimensions of extracted face images features. The classification process is conducted using L-RNN. The quasi-Newton back propagation algorithm is used to train the L-RNN. The proposed hybrid technique is examined on MUCT database and the performance analysis of different ANN techniques shows that the Hybrid technique is robust and has superior performance over conventional methods. It achieves the best classification rate of 98%.

Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining

“…With the recent development of data mining techniques, rather complex models are representing good performance. We obtained the joint distribution characteristic quantity w N (t) of the macroeconomic time series, the gain correlation characteristic quantity, and the joint parameter fusion of the associated data [20]. e joint autocorrelation characteristic analysis is performed to obtain the Doppler parameter σ n of the macroeconomic time series.…”

“…We set the measurement index parameter B L of environmental uncertainty and obtained the category number of macroeconomic time series samples (e.g., the i (i ∈ p)) and the distribution coefficient of economic time series samples [20], using the method of autocorrelation feature distributed estimation, to calculate the difference between the maximum membership degrees of each type of economic time series samples and get the detection statistical distribution coefficient value of macroeconomic time series as B L . Using the joint parameter analysis method, the macroeconomic time series prediction results are computed as follows:…”

Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

“…As ELMo [11], BERT [12] and other models have been proposed, text representation not only considers the morphological information of the word but also takes into account the context and semantic information. Recently, in the field of artificial intelligence and law, various neural network architectures such as CNN [13] and RNN [14] have been used for document embedding. Jiang et al [15] use deep reinforcement learning methods to improve classification accuracy.…”

“…For a given case, the task of LJP aims to empower machines to predict the judgment results (e.g., law articles, charges, and prison terms) of the case. Inspired by the success of deep learning techniques [13,14,21] on NLP tasks, researchers attempt to employ neural models to handle judgment prediction tasks. Some popular neural network methods are used in an automatic charge prediction task [22][23][24], and there are some works focusing on identifying applicable law articles for a given case [25][26][27].…”

Research on PM2.5 Spatiotemporal Forecasting Model Based on LSTM Neural Network

“…It has been proposed to predict future output with past inputs. Compared with the traditional recurrent neural network (RNN) [10], it is unique in that it is designed with a loop body structure that has proven to be very suitable for prediction based on time-series data. And the disappearing gradient problem has better performance than RNN [11][12][13].…”