Recognition of facial expressions using 2D DCT and neural network

Xiao, Yegui; Chandrasiri, Naiwala P.; Tadokoro, Yoshiaki; Oda, Masaomi

doi:10.1002/(sici)1520-6440(199907)82:7<1::aid-ecjc1>3.0.co;2-e

Cited by 19 publications

(18 citation statements)

References 6 publications

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“…The 2-D discrete cosine transform is used to compress the entire face image. The resulting lower-frequency 2-D discrete cosine transform coefficients are used to train a one-hidden-layer feedforward neural network in [27]. Very promising experimental results are also reported in [24].…”

Section: Introductionmentioning

confidence: 57%

“…For sake of comparison with two other methods that are available in the literature, namely the Vector Matching algorithm in [12] and the fixed-size NN algorithm in [27], simulation results are provided in Tables III and IV. A comparative summary of the performance of our proposed algorithm and the above two algorithms are given in Table V.…”

Section: B Experimental Resultsmentioning

confidence: 99%

“…The ultimate goal in this research area is the realization of intelligent and transparent communications between human beings and machines. Several facial expression recognition methods have been proposed in the literature; see, for example, [4], [5], [12], and [27] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

“…A more detailed review on facial expression recognition can be found in [4]. The neural network-based recognition methods are found to be particularly promising [24], [27], since the neural networks can easily implement the mapping from the feature space of face images to the facial expression space. However, determining a proper network size has always been a frustrating and time consuming experience for neural network developers.…”

Section: Introductionmentioning

confidence: 99%

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Facial Expression Recognition Using Constructive Feedforward Neural Networks

Khorasani

2004

IEEE Trans. Syst., Man, Cybern. B

201

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A new technique for facial expression recognition is proposed, which uses the two-dimensional (2-D) discrete cosine transform (DCT) over the entire face image as a feature detector and a constructive one-hidden-layer feedforward neural network as a facial expression classifier. An input-side pruning technique, proposed previously by the authors, is also incorporated into the constructive learning process to reduce the network size without sacrificing the performance of the resulting network. The proposed technique is applied to a database consisting of images of 60 men, each having five facial expression images (neutral, smile, anger, sadness, and surprise). Images of 40 men are used for network training, and the remaining images of 20 men are used for generalization and testing. Confusion matrices calculated in both network training and generalization for four facial expressions (smile, anger, sadness, and surprise) are used to evaluate the performance of the trained network. It is demonstrated that the best recognition rates are 100% and 93.75% (without rejection), for the training and generalizing images, respectively. Furthermore, the input-side weights of the constructed network are reduced by approximately 30% using our pruning method. In comparison with the fixed structure back propagation-based recognition methods in the literature, the proposed technique constructs one-hidden-layer feedforward neural network with fewer number of hidden units and weights, while simultaneously provide improved generalization and recognition performance capabilities.

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Section: Introductionmentioning

confidence: 57%

Section: B Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Facial Expression Recognition Using Constructive Feedforward Neural Networks

Khorasani

2004

IEEE Trans. Syst., Man, Cybern. B

201

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“…Several facial expression methods have been proposed in the literature [11,12,13]. In recent years, facial expression recognition based on two-dimensional (2-D) digital images has received a lot of attention by researchers, because it doesn't involve 3-D measurements [13] and is suitable for real time application. A more detailed review on facial expression recognition can be found in [11].…”

Section: Facial Expression Recognition Using Kmqdfmentioning

confidence: 56%

Kernel Modified Quadratic Discriminant Function for Facial Expression Recognition

Yang

Jin

Yin

et al. 2006

Advances in Machine Vision, Image Processing, and Pattern Analysis

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Abstract. The Modified Quadratic Discriminant Function was first proposed by Kimura et al to improve the performance of Quadratic Discriminant Function, which can be seen as a dot-product method by eigen-decompostion of the covariance matrix of each class. Therefore, it is possible to expand MQDF to high dimension space by kernel trick. This paper presents a new kernel-based method to pattern recognition, Kernel Modified Quadratic Discriminant Function(KMQDF), based on MQDF and kernel method. The proposed KMQDF is applied in facial expression recognition. JAFFE face database and the AR face database are used to test this algorithm. Experimental results show that the proposed KMQDF with appropriated parameters can outperform 1-NN, QDF, MQDF classifier.

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Facial Expression Recognition Techniques Using Constructive Feedforward Neural Networks and K-Means Algorithm

2009

Advances in Neuro-Information Processing

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Recognition of facial expressions using 2D DCT and neural network

Cited by 19 publications

References 6 publications

Facial Expression Recognition Using Constructive Feedforward Neural Networks

Facial Expression Recognition Using Constructive Feedforward Neural Networks

Kernel Modified Quadratic Discriminant Function for Facial Expression Recognition

Facial Expression Recognition Techniques Using Constructive Feedforward Neural Networks and K-Means Algorithm

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