Facial Expression Recognition Using Constructive Feedforward Neural Networks

Abstract

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.

Facial Expression Recognition by Statistical, Spatial Features and using Decision Tree

“…In 1999, Chen and Chang [14] proposed facial expression recognition system using 'Radial Basis Function and Multi-Layer Perceptron' with accuracy in recognition rate is 92.1% in which they extracted the facial characteristic points of the 3 organs. In 2004, Ma and Khorasani [15] proposed facial expression recognition system using 'Constructive Fees Forward Neural Networks' with accuracy in recognition rate is 93.75%. In 2011, Chaiyasit, Philmoltares and Saranya [16] proposed facial expression recognition system using 'Multilayer Perceptron with the Back-Propagation Algorithm' with the recognition rate 95.24%, in which they implements graph based facial feature extraction.…”

Advances in Artificial Neural Networks – Methodological Development and Application

“…So far, ANNs have been used in many industrial and commercial applications such as process modeling and control [52], character recognition [53], image recognition [54], credit evaluation [55], fraud detection [56,57], insurance [58], and stock forecasting [59]. In a later section, applications of ANNs in agricultural and biological engineering will be reviewed.…”

A face emotion tree structure representation with probabilistic recursive neural network modeling

“…A mean accuracy rate of 83.3% was achieved. Ma and Khorasani [58] proposed using a constructive feedforward neural network for recognizing five basic emotions including neutral. A recognition rate of 93.8% on a database of 60 subjects was achieved in which 40 subjects were used for training and 20 subjects were used for testing but without including neutral emotion in the testing.…”