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International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

A Facial Expression Recognition Method Based on Quantum Neural Networks

Li Peng, Junhua Li

Published Jan 1, 2007• 12 citations
Publisher

Abstract

The Facial Expression recognition is an important and complicated problem of pattern recognition field.In this paper, an approach to Facial Expression recognition based on multi-level transfer function Quantum Neural Networks (QNN) and multi-layer classifiers is presented.The QNN is trained and tested by the Japanese female facial expression (JAFFE) database.The experiment results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.

DOI
10.2991/iske.2007.10

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The Facial Expression recognition is an important and complicated problem of pattern recognition field.In this paper, an approach to Facial Expression recognition based on multi-level transfer function Quantum Neural Networks (QNN) and multi-layer classifiers is presented.The QNN is trained and tested by the Japanese female facial expression (JAFFE) database.The experiment results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.
The facial expression recognition is an important and complicated problem of pattern recognition field.An approach to facial expression recognition based on multi-level transfer function quantum neural networks(QNN)and multi-layer classifiers is presented in order to improve the recognition rate and recognition reliability.The QNN is trained and tested by the CMU face expression image database.The experiment results show that the method achieves excellent performance in terms of recognition rates and recognition reliability,and the superiority and potential of QNN in solving pattern recognition problems are abvious.
The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.
In this paper, an approach to face recognition is presented, which based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. Firstly, image preprocessing is used to eliminate unrelated information in face images, which could help to locate eyes position. Secondly, feature extraction employs eigenface method based on Karhunen-Loeve transform to extract statistic feature and reduce dimensions. Finally, in the part of face recognition, quantum neural networks based on multi-level transform function are used. The QNN is trained and tested by the ORL faces digital database. At the same time with the classical BP neural network classifiers are compared, the results show that the identification method in more complex environments with a certain degree of robustness and effective and feasible.
In this paper, a handwritten digital recognition system based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers is proposed. The recognition system proposed consists of two layer sub-classifiers, namely first-layer QNN coarse classifier and second-layer QNN numeral pairs classifier. Handwritten digital recognition experiments are performed by using data from MNIST database. Experiment results indicate the proposed QNN recognition system achieves excellent performance in terms of recognition rates and recognition reliability, and at the same time show the superiority and potential of QNN in solving pattern recognition problems.
The purpose of this work is to analyze how new technologies can enhance clinical practice while also examining the physical traits of emotional expressiveness of face expression in a number of psychiatric illnesses. Hence, in this work, an automatic facial expression recognition system has been proposed that analyzes static, sequential, or video facial images from medical healthcare data to detect emotions in people's facial regions. The proposed method has been implemented in five steps. The first step is image preprocessing, where a facial region of interest has been segmented from the input image. The second component includes a classical deep feature representation and the quantum part that involves successive sets of quantum convolutional layers followed by random quantum variational circuits for feature learning. Here, the proposed system has attained a faster training approach using the proposed quantum convolutional neural network approach that takes [Formula: see text] time. In contrast, the classical convolutional neural network models have [Formula: see text] time. Additionally, some performance improvement techniques, such as image augmentation, fine-tuning, matrix normalization, and transfer learning methods, have been applied to the recognition system. Finally, the scores due to classical and quantum deep learning models are fused to improve the performance of the proposed method. Extensive experimentation with Karolinska-directed emotional faces (KDEF), Static Facial Expressions in the Wild (SFEW 2.0), and Facial Expression Recognition 2013 (FER-2013) benchmark databases and compared with other state-of-the-art methods that show the improvement of the proposed system.
Abstract Since the multi-level activation function quantum neural network (QNN) for pattern recognition was firstly proposed by Purushothaman and Karayiannis, more and more researches have been conducted on improving it. However, they all ignore that the QNN only uses multi-level activation function to simulate the concept of quantum superposition rather than really using quantum computing. In this paper, we propose a real QNN model based on multi-layer activation function. In addition, we present algorithms for updating weight parameters and quantum intervals, and also improve the learning algorithm for weight parameters using famous Levenberg–Marquardt algorithm. We also use the QNN for lie detection, and the simulation results of MATLAB prove that the performance of the model based on the corresponding algorithm are very strong.