title: Constructive autoassociative neural network for facial recognition.
abstract: Autoassociative artificial neural networks have been used in many different computer vision applications. However, it is difficult to define the most suitable neural network architecture because this definition is based on previous knowledge and depends on the problem domain. To address this problem, we propose a constructive autoassociative neural network called CANet (Constructive Autoassociative Neural Network). CANet integrates the concepts of receptive fields and autoassociative memory in a dynamic architecture that changes the configuration of the receptive fields by adding new neurons in the hidden layer, while a pruning algorithm removes neurons from the output layer. Neurons in the CANet output layer present lateral inhibitory connections that improve the recognition rate. Experiments in face recognition and facial expression recognition show that the CANet outperforms other methods presented in the literature.
authors.affiliation: Escola Politécnica, Universidade de Pernambuco, Recife-PE, Brazil.
authors.forename: Bruno J T
authors.initials: BJ
authors.lastname: Fernandes
authors.affiliation: Centro de Informática, Universidade Federal de Pernambuco, Recife-PE, Brazil.
authors.forename: George D C
authors.initials: GD
authors.lastname: Cavalcanti
authors.affiliation: Centro de Informática, Universidade Federal de Pernambuco, Recife-PE, Brazil.
authors.forename: Tsang I
authors.initials: TI
authors.lastname: Ren
mesh_terms: D000465:Algorithms; D004644:Emotions; D005145:Face / Q000033:anatomy & histology*; D006801:Humans; D016571:Neural Networks, Computer